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Journal articles on the topic 'Face spoofing detection'
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1
Lai, Zhimao, Yang Guo, Yongjian Hu, Wenkang Su, and Renhai Feng. "Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach." Sensors 24, no. 24 (2024): 8075. https://doi.org/10.3390/s24248075.
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Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems. Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition. However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets featuring light makeup faces. If these instances are incorrectly flagged as fraudulent by face anti-spoofing systems, it could lead to user inconvenience. In response, we develop a face anti-spoofing database that includes light makeup faces and establishes a criterion for determining light makeup to select appropriate data. Building on this foundation, we assess multiple established face anti-spoofing algorithms using the newly created database. Our findings reveal that the majority of these algorithms experience a decrease in performance when faced with light makeup faces. Consequently, this paper introduces a general face anti-spoofing algorithm specifically designed for light makeup faces, which includes a makeup augmentation module, a batch channel normalization module, a backbone network updated via the Exponential Moving Average (EMA) method, an asymmetric virtual triplet loss module, and a nearest neighbor supervised contrastive module. The experimental outcomes confirm that the proposed algorithm exhibits superior detection capabilities when handling light makeup faces.
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Journal, IJSREM. "Anti Face Spoofing System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–12. http://dx.doi.org/10.55041/ijsrem37068.
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Face spoofing, the act of using deceptive techniques such as printed photos or digital images to deceive facial recognition systems, poses a significant threat to the security of various applications, including biometric authentication and access control systems. This paper presents a concise yet effective approach to address the challenge of anti-face spoofing using Python within a limited codebase of 50 lines. The proposed solution leverages a combination of image processing techniques and machine learning algorithms to detect and prevent face spoofing attempts. A pre-trained deep neural network model for facial recognition is employed to extract essential facial features. Subsequently, the system utilizes image manipulation detection methods to identify anomalies indicative of face spoofing attacks. The implementation showcases the simplicity and efficiency of the proposed anti-face spoofing technique, demonstrating the potential for integration into real- world applications with minimal computational overhead. The concise Python code enables easy adoption and adaptation for developers aiming to enhance the security of facial recognition systems against face spoofing threats. The experimental results demonstrate the effectiveness of the approach in accurately distinguishing between genuine and spoofed facial images, thereby contributing to the robustness of facial recognition systems in the presence of adversarial attacks. Keywords - Liveness Detection, Presentation Attack Detection, Spoof Detection Algorithms, Biometric Security, Face Recognition Robustness, 3D Face Modeling, Texture Analysis, Deep Learning Anti- Spoofing, Multi-Spectral Imaging, Behavioral Biometrics, Challenge-Response Mechanisms, Optical Flow Analysis, Pulse Detection, Surface Reflectance, Depth Sensing, Motion Analysis, Pattern Recognition, Anti-Spoofing Networks, Temporal Feature Extraction, Anomaly Detection.
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M, Parvadhi, Kishore S, Narayana Sakthivel S, Lalith Kumar K, and Prasana Kumar K. S. "ENHANCED FACE SPOOFING DETECTION THROUGH GEOMETRIC TEMPORAL DYNAMIC ANALYSIS." International Journal of Technical Research & Science 9, Spl (2024): 27–35. http://dx.doi.org/10.30780/specialissue-iset-2024/012.
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Face spoofing, the act of presenting a fake face or biometric feature to deceive authentication systems, poses a significant threat to the security of facial recognition systems. With the proliferation of biometric authentication in various applications, including mobile devices, banking, and surveillance systems, the vulnerability to face spoofing attacks has become a pressing concern. This paper provides a comprehensive review and analysis of face spoofing detection techniques, focusing on both traditional methods and recent advancements. The review begins by outlining the various types of face spoofing attacks, including printed photos, replay attacks, 3D masks, and makeup disguises. Subsequently, it discusses the challenges faced by face spoofing detection systems, such as the high variability in spoofing materials, illumination conditions, and presentation attacks. Traditional techniques, including texture analysis, motion analysis, and color-based methods, are examined, highlighting their strengths and limitations Furthermore, the paper explores recent advancements in face anti-spoofing, including deep learning-based approaches, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). It discusses the effectiveness of these techniques in mitigating the vulnerabilities of conventional methods and their ability to handle complex spoofing attacks with higher accuracy and robustness. Additionally, the review investigates datasets commonly used for training and evaluating face spoofing detection algorithms, emphasizing the importance of diverse and representative datasets for reliable performance assessment. Furthermore, it discusses evaluation metrics, benchmarking protocols, and open challenges in the field to provide insights into future research directions.
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Setyawan, Marselinus Putu Harry, I. Dewa Made Bayu Atmaja Darmawan, I. Gede Santi Astawa, and I. Wayan Santiyasa. "Bahasa Indonesia." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 13, no. 1 (2024): 179. https://doi.org/10.24843/jlk.2024.v13.i01.p17.
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This research aims to develop a spoofing detection system using the Support Vector Machine (SVM) method with texture feature extraction of Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM). The system is intended to address the challenge of student attendance by integrating face recognition technology into attendance management. Spoofing, which is an attempt to counterfeit faces, poses a challenge in face-based security systems. Therefore, the system focuses on spoofing detection by comparing texture patterns between real and fake faces. The model is able to identify face spoofing attempts with an accuracy rate of 94% after tuning the C and gamma parameters. Furthermore, the anti-spoofing attendance system is tested through black box testing and provides results that meet expectations. The system is able to start classes, record student attendance, and generate valid attendance reports. The entire system functions have been thoroughly tested and achieve a 95% accuracy rate in spoofing detection.
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Su-Gyeong Yu, Su-Gyeong Yu, So-Eui Kim Su-Gyeong Yu, Kun Ha Suh So-Eui Kim, and Eui Chul Lee Kun Ha Suh. "Effect of Facial Shape Information Reflected on Learned Features in Face Spoofing Detection." 網際網路技術學刊 23, no. 3 (2022): 517–25. http://dx.doi.org/10.53106/160792642022052303010.
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<p>Face recognition is a convenient and non-contact biometric method used widely for secure personal authentication. However, the face is an exposed body part, and face spoofing attacks, which compromise the security of systems that use face recognition for authentication, are frequently reported. Previous face spoofing attack detection studies proposed texture-analysis-based methods using handcrafted features or learned features to prevent spoofing attacks. However, it is unclear whether spoofing attack images reflect the face distortion resulting from failing to reflect the three-dimensional structure of a real face. To resolve this problem, we compared and analyzed the face spoofing attack detection performances of two typical convolutional neural network models, namely ResNet-18 and DenseNet-121. CASIA-FASD, Replay-Attack, and PR-FSAD were used as the training data. The classification performance of the model was evaluated based on four protocols. DenseNet-121 exhibited better performance in most scenarios. DenseNet-121 reflected facial shape information well by uniformly applying the learned features of both the initial and final layers during training. It is expected that this study will support the realization of spoofing technology with enhanced security.</p> <p>&nbsp;</p>
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Dave, Vani. "Spoof Detection Using Local Binary Pattern In Face." Jurnal Ilmu Komputer 13, no. 1 (2020): 39. http://dx.doi.org/10.24843/jik.2020.v13.i01.p05.
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Spoofing attack is an attempt to acquire some other’s identity or access right by using a biometric evidence of authorized user. Among all biometric systems facial identity is one of the widely used method that is prone to such spoofing attacks using a simple photograph of the user.
 The paper focuses and takes the problem area of face spoofing attacks into account by detecting spoof faces and real faces. We are using the local binary pattern (LBP) for providing the solution of spoofing problem and with the help of these patterns we inspect primarily two types of attacks i.e. printed photograph and photos displayed using digital screen. For this, we will use the local database maintained by us having the images labeled as real and spoof for the data required.
 We conclude that local binary pattern will reduce the total error rate and will show the moderate output when used across a wide set of attack types. This will enhance the efficiency of the system for detection of spoofing by using the deep learning techniques
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Akash, Chaudhary, AnkitaSingh, and Km.Yachana. "Anti Spoofing Face Detection with Convolutional Neural Networks Classifier." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 745–50. https://doi.org/10.5281/zenodo.7953326.
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The ability to detect spoofed faces has become a critical concern in various applications, such as face recognition systems, banking, and security measures. Thisresearchpresentsa simple system that can detect whether a facein video stream is spoofed or real using pre-trained models for face detection and anti-spoofing. The system uses a continuous loop to read each frame of the video stream, to assess whether a face image is real or spoof, first detect faces using the pre-trained face detection model, then crop and resize the face image. If the model predicts that the face is fake, the system draws a red rectangle around the face and displays the label "spoof." If the model predicts that the face is real, the system draws a green rectangle around the face and displays the label "real." The proposed system achieved a high accuracy rate in detecting spoofed faces, making it suitable for real-world applications.
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Zahra, Sayyam, Mohibullah Khan, Kamran Abid, Naeem Aslam, and Ejaz Ahmad Khera. "A Novel Face Spoofing Detection Using hand crafted MobileNet." VFAST Transactions on Software Engineering 11, no. 2 (2023): 34–42. http://dx.doi.org/10.21015/vtse.v11i2.1485.
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There are several uses for face spoofing detection, including human-robot communication, business, film, hotel services, and even politics. Despite the adoption of numerous supervised and unsupervised techniques in a wide range of domains, proper analysis is still lacking. As a result, we chose this difficulty as our study problem. We have put out a method for the effective and precise classification of face spoofing that may be used for a variety of everyday issues. This work attempts to investigate the ideal method and parameters to offer a solution for a powerful deep learning spoofing detection system. In this study, we used the LCC FASD dataset and deep learning algorithms to recognize faces from photos. Precision and accuracy are used as the evaluation measures to assess the performance of the CNN (Convolutional Neural Network) model. The results of the studies demonstrate that the model was effective at spoofing face picture detection. The accuracy of the CNN model was 0.98. Overall, the study's findings show that spoofing detection from photos using the LCC FASD dataset can be successfully performed utilizing deep learning algorithms. Yet, the findings of this study offer a strong framework for further investigation in this area.
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Kim, Seung-Hyun, Su-Min Jeon, and Eui Chul Lee. "Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal." Sensors 22, no. 8 (2022): 3070. http://dx.doi.org/10.3390/s22083070.
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Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that minimizes the susceptibility to certain database dependencies and high-quality replay attacks without additional devices. The proposed method has the following advantages. First, because only an RGB camera is used to detect spoofing attacks, the proposed method is highly usable in various mobile environments. Second, solutions are incorporated in the method to obviate new attack scenarios that have not been previously dealt with. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that improves susceptibility to certain database dependencies and high-quality replay attack, which are the limitations of previous methods without additional devices. In the experiment, we also verified the cut-off attack scenario in the jaw and cheek area where the proposed method can be counter-attacked. By using the time series feature and the frequency feature of the remote photoplethysmography signal, it was confirmed that the accuracy of spoof detection was 99.7424%.
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Sun, Xudong, Lei Huang, and Changping Liu. "Multispectral face spoofing detection using VIS–NIR imaging correlation." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (2018): 1840003. http://dx.doi.org/10.1142/s0219691318400039.
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With the wide applications of face recognition techniques, spoofing detection is playing an important role in the security systems and has drawn much attention. This research presents a multispectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging, which exploits VIS–NIR image consistency for spoofing detection. First, we use part-based methods to extract illumination robust local descriptors, and then the consistency is calculated to perform spoofing detection. In order to further exploit multispectral correlation in local patches and to be free from manually chosen regions, we learn a confidence factor map for all the patches, which is used in final classifier. Experimental results of self-collected datasets, public Msspoof and PolyU-HSFD datasets show that the proposed approach gains promising results for both intra-dataset and cross-dataset testing scenarios, and that our method can deal with different illumination and both photo and screen spoofing.
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Pandey, Aparna, and Arvind Kumar Tiwari. "ADVANCING FACE SPOOFING DETECTION WITH LBP, PCA, AND SVM: A ROBUST AI SECURITY APPROACH." ICTACT Journal on Image and Video Processing 15, no. 4 (2025): 3589–95. https://doi.org/10.21917/ijivp.2025.0508.
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This work introduces a robust method for distinguishing between genuine and fake faces, addressing the crucial issue of biometric spoofing in AI-driven security systems. The proposed approach integrates Local Binary Pattern (LBP) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification. Evaluations demonstrate the method’s superior performance in face spoofing detection, achieving an overall detection accuracy of 96.7% in cross-validation, surpassing traditional methods such as Random Forest (94.5%). LBP extracts distinctive textural features, which are normalized for uniformity across samples. PCA reduces the dimensionality of the data by eliminating redundant information, maintaining only the most relevant features for analysis. The SVM classifier identifies patterns to differentiate genuine faces from spoofed ones, achieving high accuracy across diverse attack types. For instance, the proposed method achieves 98.1% accuracy for detecting printed photo attacks and 80.9% accuracy for challenging deepfake attacks on the created dataset, outperforming Random Forest by 1.2% and 1.1%, respectively. This comprehensive evaluation highlights the method’s robustness, computational efficiency, and adaptability to various spoofing scenarios. With consistent performance improvements across datasets, this technique addresses critical AI security challenges and provides a scalable solution for advanced face spoofing detection systems.
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Abduh, Latifah, Luma Omar, and Ioannis Ivrissimtzis. "Anomaly Detection with Transformer in Face Anti-spoofing." Journal of WSCG 31, no. 1-2 (2023): 91–98. http://dx.doi.org/10.24132/jwscg.2023.10.
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Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNet as the backbone for anomaly detection in face anti-spoofing and compare the performance of various one-class classifiers at the end of the pipeline, such as one-class SVM, Isolation Forest, and decoders. Test results on the RA and SiW databases show the proposed approach to be competitive as an anomaly detection method for face anti-spoofing.
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Aziz, Azim Zaliha Abd, and Mohd Rizon Mohamed Juhari. "Face spoofing detection using surface and sub-surface reflections analysis." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 189. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp189-197.
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Reflection based analysis has been used in previous research for various objectives. Materials classification is one of them. Basically, each material consists of two types of reflections: surface and sub-surface. To separate these two reflections, polarized light could be applied. Previously, multi-reflections characteristics were analyzed using polarized light to classify objects such as between metals and non-metals. However, no trial has been done using the same method to distinguish real and fake faces that could be used to combat spoofing attempts in face biometric system. Since human skin is multi layers structure, it also produces multi reflections. In this paper, driven by the theory, surface and sub-surface reflections of both genuine human face and paper face mask were statistically examined. In addition, iPad displayed face images were also used as spoofing attempts. Images of genuine and spoofing faces were captured using polarized light under two different polarization angles: 0 and 90 degrees. Each angle captured images with surface and sub-surface reflections, accordingly. Those reflections were analyzed based on the mean, standard deviation, skewness and kurtosis. Modality distribution of each image was also studied using another method called the bimodality coefficient (BC). From the results, it is not possible to distinguish between genuine face and printed photos because of the multi reflections’ similarities. However, iPad displayed face images have been successfully identified as spoofing trials.
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Aziz, Azim Zaliha Abd, and Mohd Rizon Mohamed Juhari. "Face spoofing detection using surface and sub-surface reflections analysis." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 189–97. https://doi.org/10.11591/ijeecs.v24.i1.pp189-197.
Full textAbstract:
Reflection based analysis has been used in previous research for various objectives. Materials classification is one of them. Basically, each material consists of two types of reflections: surface and sub-surface. To separate these two reflections, polarized light could be applied. Previously, multireflections characteristics were analyzed using polarized light to classify objects such as between metals and non-metals. However, no trial has been done using the same method to distinguish real and fake faces that could be used to combat spoofing attempts in face biometric system. Since human skin is multi layers structure, it also produces multi reflections. In this paper, driven by the theory, surface and sub-surface reflections of both genuine human face and paper face mask were statistically examined. In addition, iPad displayed face images were also used as spoofing attempts. Images of genuine and spoofing faces were captured using polarized light under two different polarization angles: 0 and 90 degrees. Each angle captured images with surface and sub-surface reflections, accordingly. Those reflections were analyzed based on the mean, standard deviation, skewness and kurtosis. Modality distribution of each image was also studied using another method called the bimodality coefficient (BC). From the results, it is not possible to distinguish between genuine face and printed photos because of the multi reflections’ similarities. However, iPad displayed face images have been successfully identified as spoofing trials.
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Megawan, Sunario, Wulan Sri Lestari, and Apriyanto Halim. "Deteksi Non-Spoofing Wajah pada Video secara Real Time Menggunakan Faster R-CNN." Journal of Information System Research (JOSH) 3, no. 3 (2022): 291–99. http://dx.doi.org/10.47065/josh.v3i3.1519.
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Face non-spoofing detection is an important job used to ensure authentication security by performing an analysis of the captured faces. Face spoofing is the process of fake faces by other people to gain illegal access to the biometric system which can be done by displaying videos or images of someone's face on the monitor screen or using printed images. There are various forms of attacks that can be carried out on the face authentication system in the form of face sketches, face photos, face videos and 3D face masks. Such attacks can occur because photos and videos of faces from users of the facial authentication system are very easy to obtain via the internet or cameras. To solve this problem, in this research proposes a non-spoofing face detection model on video using Faster R-CNN. The results obtained in this study are the Faster R-CNN model that can detect non-spoof and spoof face in real time using the Raspberry Pi as a camera with a frame rate of 1 fps.
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Enas, A. Raheem, Mumtazah Syed Ahmad Sharifah, and Azizun Wan Adnan Wan. "Insight on face liveness detection: A systematic literature review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5165–75. https://doi.org/10.11591/ijece.v9i6.pp5165-5175.
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To review researcher"s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literature. The final number of articles considered is 65 articles. 4 of them where review and survey articles that described a general overview about liveness detection and anti-spoofing methods. Since 2012, and despite of leaving some areas unestablished and needs more attention, researchers tried to keep track of liveness detection in several ways. No matter what their category is, articles concentrated on challenges that faces the full utility of anti-spoofing methods and recommended some solutions to overcome these challenges. In this paper, different types of liveness detection and face anti-spoofing techniques are investigated to keep researchers updated with what is being developed in this field.
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Bok, Jin Yeong, Kun Ha Suh, and Eui Chul Lee. "Verifying the Effectiveness of New Face Spoofing DB with Capture Angle and Distance." Electronics 9, no. 4 (2020): 661. http://dx.doi.org/10.3390/electronics9040661.
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Face recognition is a representative biometric that can be easily used; however, spoofing attacks threaten the security of face biometric systems by generating fake faces. Thus, it is not advisable to only consider sophisticated spoofing cases, such as three-dimensional masks, because they require additional equipment, thereby increasing the implementation cost. To prevent easy face spoofing attacks through print and display, the two-dimensional (2D) image analysis method using existing face recognition systems is reasonable. Therefore, we proposed a new database called the “pattern recognition-face spoofing advancement database” that can be used to prevent such attacks based on 2D image analysis. To the best of our knowledge, this is the first face spoofing database that considers the changes in both the angle and distance. Therefore, it can be used to train various positional relationships between a face and camera. We conducted various experiments to verify the efficiency of this database. The spoofing detection accuracy of our database using ResNet-18 was found to be 96.75%. The experimental results for various scenarios demonstrated that the spoof detection performances were better for images with pinch angle, near distance images, and replay attacks than those for front images, far distance images, and print attacks, respectively. In the cross-database verification result, the performance when tested with other databases (DBs) after training with our DB was better than the opposite. The results of cross-device verification in terms of camera type showed negligible difference; thus, it was concluded that the type of image sensor does not affect the detection accuracy. Consequently, it was confirmed that the proposed DB that considers various distances, capture angles, lighting conditions, and backgrounds can be used as a training DB to detect spoofing attacks in general face recognition systems.
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Sudhakar Tiwari. "Biometric Authentication in the Face of Spoofing Threats: Detection and Defense Innovations." Innovative Research Thoughts 9, no. 5 (2023): 402–20. https://doi.org/10.36676/irt.v9.i5.1583.
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Biometric authentication systems have emerged as a critical method of ensuring secure access control across various domains, from mobile phones to financial transactions. However, the systems are increasingly vulnerable to spoofing attacks, where imposter individuals attempt to deceive the biometric sensors using counterfeit biometrics, like images, 3D prints, or printed fingerprints. Spoofing attack development poses a significant threat to the security and reliability of biometric authentication systems. Current defense mechanisms are typically ineffective in providing real-time detection and adequate protection against sophisticated spoofing attacks. This research aims to fill the large gap in current biometric security systems using research into new detection and defense methods against spoofing attacks. The core focus is on designing advanced algorithms and machine learning models capable of detecting subtle but noticeable artifacts in biometric information that point to manipulation or forgery. Secondly, the research delves into multi-modal biometric systems that combine various biometric modalities (e.g., face recognition, fingerprint, and iris scanning) to enhance spoofing resilience. The research also delves into the integration of explainable AI practices such that detection models not only exhibit high accuracy but also provide interpretable and transparent results. This research aims to enhance the resistance of biometric systems to emerging spoofing methods with the innovations proposed, thereby offering a practical solution to a real problem for existing authentication technology. The results will help to make the security and reliability of biometric identification more robust in many sensitive sectors, including financial institutions, government security, and personal devices.
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Vinutha, H., and G. Thippeswamy. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. https://doi.org/10.11591/csit.v4i1.pp1-13.
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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.
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Raheem, Enas A., Sharifah Mumtazah Syed Ahmad, and Wan Azizun Wan Adnan. "Insight on face liveness detection: A systematic literature review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5865. http://dx.doi.org/10.11591/ijece.v9i6.pp5865-5175.
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<p>To review researcher’s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literature.<em> </em>The final number of articles considered is 65 articles. 4 of them where review and survey articles that described a general overview about liveness detection and anti-spoofing methods. Since 2012, and despite of leaving some areas unestablished and needs more attention, researchers tried to keep track of liveness detection in several ways. No matter what their category is, articles concentrated on challenges that faces the full utility of anti-spoofing methods and recommended some solutions to overcome these challenges. In this paper, different types of liveness detection and face anti-spoofing techniques are investigated to keep researchers updated with what is being developed in this field.</p>
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Mohamed, Shaimaa, Amr Ghoneim, and Aliaa Youssif. "Visible/Infrared face spoofing detection using texture descriptors." MATEC Web of Conferences 292 (2019): 04006. http://dx.doi.org/10.1051/matecconf/201929204006.
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With extensive applications of face recognition technologies, face anti-spoofing played an important role and has drawn a great attention in the security systems. This study represents a multi-spectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging. Spectral imaging is the capture of images in multiple bands. Since these attacks are carried out at the sensor, operating in the visible range, a sensor operating in another band can give more cues regarding the artifact or disguise used to carry out the attack. Our experimental results of public datasets proved that the proposed algorithms gain promising results for different testing scenarios and that our methods can deal with different illuminations and both photo and screen spoofing.
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Arti, Yuni, and Aniati Murni Arymurthy. "Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal." Jurnal Ilmu Komputer dan Informasi 16, no. 1 (2023): 47–57. http://dx.doi.org/10.21609/jiki.v16i1.1100.
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Face spoofing can provide inaccurate face verification results in the face recognition system. Deep learning has been widely used to solve face spoofing problems. In face spoofing detection, it is unnecessary to use the entire network layer to represent the difference between real and spoof features. This study detects face spoofing by cutting the Inception-v3 network and utilizing RGB modal, depth, and fusion approaches. The results showed that face spoofing detection has a good performance on the RGB and fusion models. Both models have better performance than the depth model because RGB modal can represent the difference between real and spoof features, and RGB modal dominate the fusion model. The RGB model has accuracy, precision, recall, F1-score, and AUC values obtained respectively 98.78%, 99.22%, 99.31.2%, 99.27%, and 0.9997 while the fusion model is 98.5%, 99.31%, 98.88%. 99.09%, and 0.9995, respectively. Our proposed method with cutting the Inception-v3 network to mixed6 successfully outperforms the previous study with accuracy up to 100% using the MSU MFSD benchmark dataset.
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Jagdale, Prasad A., and Sudeep D. Thepade. "Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 8 (2019): 19–22. http://dx.doi.org/10.17762/ijritcc.v7i8.5348.
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Nowadays the system which holds private and confidential data are being protected using biometric password such as finger recognition, voice recognition, eyries and face recognition. Face recognition match the current user face with faces present in the database of that security system and it has one major drawback that it never works better if it doesn’t have liveness detection. These face recognition system can be spoofed using various traits. Spoofing is accessing a system software or data by harming the biometric recognition security system. These biometric systems can be easily attacked by spoofs like peoples face images, masks and videos which are easily available from social media. The proposed work mainly focused on detecting the spoofing attack by training the system. Spoofing methods like photo, mask or video image can be easily identified by this method. This paper proposed a fusion technique where different features of an image are combining together so that it can give best accuracy in terms of distinguish between spoof and live face. Also a comparative study is done of machine learning classifiers to find out which classifiers gives best accuracy.
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Hatture, Sanjeeva Kumar M., and Shweta Policepatil. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (2021): 225–32. http://dx.doi.org/10.35940/ijrte.b6309.0710221.
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Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users’ artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today's world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.
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Shweta, Policepatil, and Kumar M. Hatture Sanjeeva. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (2021): 225–32. https://doi.org/10.35940/ijrte.B6309.0710221.
Full textAbstract:
Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users’ artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today's world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.
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Anand, Diksha, and Kamal Gupta. "Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique: A Review." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (2018): 51. http://dx.doi.org/10.23956/ijarcsse.v8i6.722.
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Face recognition is an alternative means to authenticate a person in different applications for access control. Instead of many improvements, this method is prone to various attacks like photos, 3D masks and video replay attack. Due to these attacks, system should require a face spoof detection system. A face spoof detection systems have an ability to identify whether a face is from a real person or a fake image. Face spoofing effect the image by adding deformation in it and also degrades the image pattern quality. Face spoofing detection system automatically identifies the human face is a true face or a fake face. In today's era, face recognition method is widely used to authenticate the face (like for unlocking mobile phones etc.) and providing access to the services or facilities but some intruders use various trick to crack the authentication system by presenting the false face in front of the authentication system, so it become necessity to prevent our face authentication system from face spoofing attack. So the choice of the technique to detect the face spoofing attack should be accurate and highly efficient.
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Albakri, Ghazel, and Sharifa Alghowinem. "The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras." Sensors 19, no. 8 (2019): 1928. http://dx.doi.org/10.3390/s19081928.
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Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.
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Tirunagari, Santosh, Norman Poh, David Windridge, Aamo Iorliam, Nik Suki, and Anthony T. S. Ho. "Detection of Face Spoofing Using Visual Dynamics." IEEE Transactions on Information Forensics and Security 10, no. 4 (2015): 762–77. http://dx.doi.org/10.1109/tifs.2015.2406533.
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Boulkenafet, Zinelabidine, Jukka Komulainen, and Abdenour Hadid. "Face Spoofing Detection Using Colour Texture Analysis." IEEE Transactions on Information Forensics and Security 11, no. 8 (2016): 1818–30. http://dx.doi.org/10.1109/tifs.2016.2555286.
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Edmunds, Taiamiti, and Alice Caplier. "Face spoofing detection based on colour distortions." IET Biometrics 7, no. 1 (2017): 27–38. http://dx.doi.org/10.1049/iet-bmt.2017.0077.
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Verissimo, Sandoval, Guilherme Gadelha, Leonardo Batista, João Janduy, and Fabio Falcão. "Transfer learning for face anti-spoofing detection." IEEE Latin America Transactions 21, no. 4 (2023): 530–36. http://dx.doi.org/10.1109/tla.2023.10128884.
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Lonkar, Nikita Shrikant. "Banking Security System with Face Liveness Detection Using Machine Learning and Image Processing." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1334–38. https://doi.org/10.22214/ijraset.2025.67510.
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The face is a significant part of the human body, recognizing people in enormous gatherings. Subsequently, on account of its all-inclusiveness and uniqueness, it has turned into the most generally utilized and acknowledged biometric strategy. Biometrics with facial recognition is now widely used. A face identification system should identify not only someone's faces but also detect spoofing attempts with printed face or digital presentations. A sincere spoofing prevention approach is to examine face liveness, such as eye blinking and lips movement. Nevertheless, this approach is helpless when dealing with videobased replay attacks. For this reason, this paper proposes a combined method of face liveness detection and CNN (Convolutional Neural Network) classifier. The anti-spoofing method is designed with two modules, the blinking eye module that evaluates eye openness and lip movement, and the CCN classifier module. The dataset for training our CNN classification can be from a variety of publicly available sources. The test results show that the module created can recognize various kinds of facial spoof attacks, such as using posters, masks, or smartphones.
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H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.p1-13.
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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.
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Hashmi, Mohammad Adil ullah. "Study of Machine Learning Algorithm based on Face Anti-Spoofing Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28013.
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Face spoofing detection is one of the most well-studied problems in computer vision. Face recognition has become a widely adopted technique in biometric authentication systems. In face recognition based authentication techniques, the system first recognized the person to verify the legitimacy of the user before granting access to the system resources. The system must be able to determine the liveness of the person in front of the camera, for example, by recognizing the face and denying the types of face presentation attacks related to photographs, videos and the 3D mask of the targeted person. Attackers try to directly or indirectly, masquerade the biometric system as another person by forging biometric traits and get unauthorized access. This work studies computer vision- based feature extraction techniques for real and spoof face imaging and combines different features in the area of face anti-spoofing. Keywords: - Face Spoofing, Face Recognition, Machine Learning (ML)
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Pujol, Francisco A., María José Pujol, Carlos Rizo-Maestre, and Mar Pujol. "Entropy-Based Face Recognition and Spoof Detection for Security Applications." Sustainability 12, no. 1 (2019): 85. http://dx.doi.org/10.3390/su12010085.
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Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches.
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H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.pp1-13.
Full textAbstract:
The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.
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Jia, Shan, Chuanbo Hu, Xin Li, and Zhengquan Xu. "Face spoofing detection under super-realistic 3D wax face attacks." Pattern Recognition Letters 145 (May 2021): 103–9. http://dx.doi.org/10.1016/j.patrec.2021.01.021.
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Desai, Khyati Jash, and Sunil Kumar. "A Study on Face Recognition and Face Spoofing Detection Techniques." International Journal of Computer Applications 185, no. 14 (2023): 24–29. http://dx.doi.org/10.5120/ijca2023922823.
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Shinde, Pratibha, and Raundale Ajay R. "Face and liveness detection with criminal identification using machine learning and image processing techniques for security system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 722–29. https://doi.org/10.11591/ijai.v13.i1.pp722-729.
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In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
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Khairnar, Smita, Shilpa Gite, Ketan Kotecha, and Sudeep D. Thepade. "Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions." Big Data and Cognitive Computing 7, no. 1 (2023): 37. http://dx.doi.org/10.3390/bdcc7010037.
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Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using the PRISMA approach by exploring the most relevant electronic databases. The article selection process follows preset inclusion and exclusion criteria. The conceptual analysis examines the data retrieved from the selected papers. To the author, this is one of the foremost systematic literature reviews dedicated to face-liveness detection that evaluates existing academic material published in the last decade. The research discusses face spoofing attacks, various feature extraction strategies, and Artificial Intelligence approaches in face liveness detection. Artificial intelligence-based methods, including Machine Learning and Deep Learning algorithms used for face liveness detection, have been discussed in the research. New research areas such as Explainable Artificial Intelligence, Federated Learning, Transfer learning, and Meta-Learning in face liveness detection, are also considered. A list of datasets, evaluation metrics, challenges, and future directions are discussed. Despite the recent and substantial achievements in this field, the challenges make the research in face liveness detection fascinating.
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Tsai, Yao-Hong, and Yu-Jung Lin. "Face Spoofing Detection Based on Local Binary Descriptors." Electronic Imaging 2017, no. 13 (2017): 105–8. http://dx.doi.org/10.2352/issn.2470-1173.2017.13.ipas-208.
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Daniel, Neenu, and A. Anitha. "Texture and quality analysis for face spoofing detection." Computers & Electrical Engineering 94 (September 2021): 107293. http://dx.doi.org/10.1016/j.compeleceng.2021.107293.
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de Souza, Gustavo Botelho, Daniel Felipe da Silva Santos, Rafael Goncalves Pires, Aparecido Nilceu Marana, and Joao Paulo Papa. "Deep Texture Features for Robust Face Spoofing Detection." IEEE Transactions on Circuits and Systems II: Express Briefs 64, no. 12 (2017): 1397–401. http://dx.doi.org/10.1109/tcsii.2017.2764460.
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Song, Xiao, Xu Zhao, Liangji Fang, and Tianwei Lin. "Discriminative representation combinations for accurate face spoofing detection." Pattern Recognition 85 (January 2019): 220–31. http://dx.doi.org/10.1016/j.patcog.2018.08.019.
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Li, Lei, Xiaoyi Feng, Zhaoqiang Xia, Xiaoyue Jiang, and Abdenour Hadid. "Face spoofing detection with local binary pattern network." Journal of Visual Communication and Image Representation 54 (July 2018): 182–92. http://dx.doi.org/10.1016/j.jvcir.2018.05.009.
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Bhupelli Prem and Kalidas. "DEEP TEXTURE FEATURES FOR ROBUST FACE SPOOFING DETECTION." international journal of engineering technology and management sciences 6, no. 6 (2022): 522–26. http://dx.doi.org/10.46647/ijetms.2022.v06i06.090.
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Biometrics arose as a vigorous answer for security frameworks. Notwithstanding, given the spread of biometric applications, lawbreakers are creating procedures to evade them by reenacting physical or social characteristics of legitimate clients (caricaturing assaults). In spite of face being a promising trademark because of its comprehensiveness, worthiness and presence of cameras all over the place, face acknowledgment frameworks are very helpless against such cheats since they can be effectively messed with normal printed facial photos. Cutting edge draws near, in view of Convolutional Brain Organizations (CNNs), present great outcomes in face ridiculing discovery. Nonetheless, these techniques don't consider the significance of advancing profound nearby elements from every facial locale, despite the fact that it is known from face acknowledgment that every facial area presents different visual angles, which can likewise be taken advantage of for face satirizing location. In this work we propose a clever CNN design prepared in two stages for such undertaking. At first, each piece of the brain network gains highlights from a given facial district. Subsequently, the entire model is adjusted overall facial pictures. Results show that such pre-preparing step permits the CNN to learn different nearby caricaturing signals, working on the exhibition and the assembly speed of the last model, beating the cutting edge draws near.
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Basurah, Muhammad, Windra Swastika, and Oesman Hendra Kelana. "IMPLEMENTATION OF FACE RECOGNITION AND LIVENESS DETECTION SYSTEM USING TENSORFLOW.JS." Jurnal Informatika Polinema 9, no. 4 (2023): 509–16. http://dx.doi.org/10.33795/jip.v9i4.1332.
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Facial recognition is a popular biometric security system used to authenticate individuals based on their unique facial structure. However, this system is vulnerable to spoofing attacks where the attacker can bypass the system using fake representations of the user's face such as photos, statues or videos. Liveness detection is a method used to address this issue by verifying that the user is a real person and not a representation. This journal article focuses on the life sign method of liveness detection, which utilizes facial movements to confirm the user's existence. We implement the latest technology of artificial intelligence from TensorFlow.js using face-api.js and compare it with the GLCM algorithm. However, even with the life sign detection method, there is still a chance of bypassing the system if an attacker uses a video recording. To mitigate this, we propose the addition of an object detection system to detect the hardware used to show video recordings with ml5.js. Our face recognition and expression detection system, using the pre-trained model face-api.js, achieved an accuracy of 85% and 82.5%, respectively, and the object detection system built with ml5.js has high accuracy and is very effective for liveness detection. Our results indicate that face-api.js outperformed GLCM algorithm in detecting spoofing attempts.
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Hosen, Md Apu, Shahadat Hoshen Moz, Md Mahamudul Hasan Khalid, Sk Shalauddin Kabir, and Dr Syed Md Galib. "Face recognition-based attendance system with anti-spoofing, system alert, and email automation." Radioelectronic and Computer Systems, no. 2 (May 25, 2023): 119–28. http://dx.doi.org/10.32620/reks.2023.2.10.
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The subject matter of the article is the design of an attendance system based on face recognition with anti-spoofing, system alarm, and Email Automation to improve accuracy and efficiency, highlighting its potential to revolutionize traditional attendance tracking methods. The administration of attendance might be a tremendous load on the authority if it is done manually. Therefore, the goal of this study is to design a reliable and efficient attendance system that can replace traditional manual approaches while also detecting and preventing spoofing attempts. Without the manual approach, attendance may be collected using many kinds of technologies, including biometric systems, radiofrequency card systems, and facial recognition systems. The face recognition attendance system stands out among the rest as a great alternative to the traditional attendance system used in offices and classrooms. The tasks to be accomplished include selecting appropriate facial detection and recognition technologies, implementing anti-spoofing measures to prevent intruders from exploiting the system, and integrating system alarms and email automation to improve accuracy and efficiency. The methods used include selecting the Haar cascade for facial detection and the LBPH algorithm for facial recognition, using DoG filtering with Haar for anti-spoofing, and implementing a speech system alarm for detecting intruders. The result of the system is a face recognition rate of 87 % and a false positive rate of 15 %. However, since the recognition rate is not 100 %, attendance will also be informed through email automation in case someone is present but is not detected by the system. In conclusion, the designed attendance system offers an effective and efficient alternative to the traditional attendance system used in offices and classrooms, providing accurate attendance records while also preventing spoofing attempts and notifying authorities of any intruders.
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Han, Hotaek, and Unsang Park. "Video Based Face Spoofing Detection Using Fourier Transform and Dense-SIFT." Journal of KIISE 42, no. 4 (2015): 483–86. http://dx.doi.org/10.5626/jok.2015.42.4.483.
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Zuama, Leygian Reyhan, De Rosal Ignatius Moses Setiadi, Ajib Susanto, Stefanus Santosa, Hong-Seng Gan, and Arnold Adimabua Ojugo. "High-Performance Face Spoofing Detection using Feature Fusion of FaceNet and Tuned DenseNet201." Journal of Future Artificial Intelligence and Technologies 1, no. 4 (2025): 385–400. https://doi.org/10.62411/faith.3048-3719-62.
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Face spoofing detection is critical for biometric security systems to prevent unauthorized access. This study proposes a deep learning-based approach integrating FaceNet and DenseNet201 to enhance face spoofing detection performance. FaceNet generates identity-based embeddings, ensuring robust facial feature representation, while DenseNet201 extracts complementary texture-based features. These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. Ablation studies reveal that data augmentation does not always enhance performance, particularly on high-complexity datasets such as LCC-FASD, where augmentation increases the False Rejection Rate (FRR). Conversely, DenseNet201 benefits more from augmentation, while the proposed method performs best without augmentation. Comparative analysis with previous studies further confirms the superiority of the proposed approach in reducing error rates, particularly Half Total Error Rate (HTER), False Acceptance Rate (FAR), and FRR. These findings indicate that combining identity-based embeddings and texture-based feature extraction significantly improves spoofing detection and enhances model robustness across different attack scenarios. This study advances biometric security by introducing an efficient feature fusion strategy that strengthens deep learning-based spoof detection. Future research may explore further optimization strategies and evaluate the approach on more diverse datasets to enhance generalization.