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Journal articles on the topic 'Face spoof detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Ms., Dilna e. p1, Maneesha Manoj2 Ms., Jiji c. j3 Ms., Jeena c. j. Ms., and Hrudhya k. p5 Ms. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research 4, no. 1 (2017): 40–48. https://doi.org/10.5281/zenodo.10774726.

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<strong>Abstract: </strong>   <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose a

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Ms., Dilna e. p1 Ms. Maneesha Manoj2 Ms. Jiji c. j3 Ms. Jeena c. j. 4. Ms. Hrudhya k. p5. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research, ISSN: 2349 –3607 (Online) , ISSN: 2349 –4824 (Print) Vol.4,, Issue 1, Jan-2017, (2017): pp 40–48. https://doi.org/10.5281/zenodo.242479.

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<strong>Abstract: </strong> <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient a

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Mittal, Abhishek, Pravneet Kaur, and Dr Ashish Oberoi. "Hybrid Algorithm for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 1028–37. http://dx.doi.org/10.22214/ijraset.2022.40452.

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Abstract: The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that th

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Balamurali, K., S. Chandru, Muhammed Sohail Razvi, and V. Sathiesh Kumar. "Face Spoof Detection Using VGG-Face Architecture." Journal of Physics: Conference Series 1917, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/1917/1/012010.

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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

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Pal, Lovely, and Renuka Singh. "A Face Spoof Detection using Feature Extraction and SVM." International Journal of Science and Research (IJSR) 11, no. 11 (2022): 629–34. http://dx.doi.org/10.21275/sr221103150851.

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Singh, Km Priyanka, Dr Pushpneel Verma, and Ajay Singh. "Technique of Face Spoof Detection using Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1435–38. http://dx.doi.org/10.22214/ijraset.2022.46847.

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Abstract: Face detection is one in every of the foremost relevent application of image processing and biometric system. Artificial neural networks (ANN) are utilized in the sphere of image processing and pattern recognition. For the recognition and detection of spoofed and non-spoofed images, face spoof approach was proposed. Earlier presented support vector machine classification model is used for the detection of spoofed or non-spoofed images. within the earlier research, SVM based approach was proposed to detect the face spoof. The face spoof detection approaches involves two stages. The in

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Mittal, Abhishek. "Hybrid Classification for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1732–39. http://dx.doi.org/10.22214/ijraset.2021.39085.

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Abstract: ML (machine learning) is consisted of a method of recognizing face. This technique is useful for the attendance system. Two sets are generated for testing and training phases in order to segment the image, to extract the features and develop a dataset. An image is considered as a testing set; the training set is contrasted when it is essential to identify an image. An ensemble classifier is implemented to classify the test images as recognized or non-recognized. The ensemble algorithm fails to acquire higher accuracy as it classifies the data in two classes. Thus, GLCM (Grey Level Co

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Tamtama, Gabriel Indra Widi, and I. Kadek Dendy Senapartha. "Fake Face Detection System Using MobileNets Architecture." CESS (Journal of Computer Engineering, System and Science) 8, no. 2 (2023): 329. http://dx.doi.org/10.24114/cess.v8i2.43762.

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Sistem pengenalan wajah merupakan salah satu metode dalam teknik biometric yang menggunakan wajah untuk proses identifikasi atau verifikasi seseorang. Teknologi ini tidak memerlukan kontak fisik seperti verifikasi sidik jari dan diklaim lebih aman karena wajah setiap orang memiliki karakter yang berbeda-beda. Terdapat dua fase utama dalam sistem biometrik wajah, yaitu deteksi wajah palsu Presentation Attack (PA) detektor dan pengenalan wajah (face recognition). Penelitian ini melakukan eksperimen dengan tujuan membangun sebuah model pembelajaran mesin (machine learning) berbasis mobile untuk m

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Kaur, Ramandeep, and P. S. "Techniques of Face Spoof Detection: A Review." International Journal of Computer Applications 164, no. 1 (2017): 29–33. http://dx.doi.org/10.5120/ijca2017913569.

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Di Wen, Hu Han, and Anil K. Jain. "Face Spoof Detection With Image Distortion Analysis." IEEE Transactions on Information Forensics and Security 10, no. 4 (2015): 746–61. http://dx.doi.org/10.1109/tifs.2015.2400395.

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Patel, Keyurkumar, Hu Han, and Anil K. Jain. "Secure Face Unlock: Spoof Detection on Smartphones." IEEE Transactions on Information Forensics and Security 11, no. 10 (2016): 2268–83. http://dx.doi.org/10.1109/tifs.2016.2578288.

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Farmanbar, Mina, and Önsen Toygar. "Spoof detection on face and palmprint biometrics." Signal, Image and Video Processing 11, no. 7 (2017): 1253–60. http://dx.doi.org/10.1007/s11760-017-1082-y.

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Muradkhanli, Leyla, and Parviz Namazli. "FACE SPOOF DETECTION USING CONVOLUTIONAL NEURAL NETWORK." Problems of Information Society 14, no. 2 (2023): 40–46. http://dx.doi.org/10.25045/jpis.v14.i2.05.

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The paper suggests a technique that uses convolutional neural network (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fake facial images. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, a standardized data

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Akhtar, Zahid, and Gian Luca Foresti. "Face Spoof Attack Recognition Using Discriminative Image Patches." Journal of Electrical and Computer Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4721849.

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Face recognition systems are now being used in many applications such as border crossings, banks, and mobile payments. The wide scale deployment of facial recognition systems has attracted intensive attention to the reliability of face biometrics against spoof attacks, where a photo, a video, or a 3D mask of a genuine user’s face can be used to gain illegitimate access to facilities or services. Though several face antispoofing or liveness detection methods (which determine at the time of capture whether a face is live or spoof) have been proposed, the issue is still unsolved due to difficulty

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Prasad, Mayank, Sandhya Jain, Praveen Bhanodia, and Anu Priya. "Influence of Standalone and Ensemble Classifiers in Face Spoofing Detection using LBP and CNN Models." European Journal of Electrical Engineering and Computer Science 8, no. 2 (2024): 17–30. http://dx.doi.org/10.24018/ejece.2024.8.2.604.

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Background: Artificial intelligence has made significant contributions to facial recognition and biometric identification and is now being employed in a range of applications. Detecting facial spoofing, where someone attempts to pass as an authorized user to gain access to the system, is still difficult. Spoofing-attack-resistant face recognition systems demand efficient and effective solutions. A more stringent recognition system will result in higher false positives and false negatives, which makes such a system questionable for practical use. Eventually, the prominent deep-learning techniqu

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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 f

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Guo, Jinlin, Yancheng Zhao, and Haoran Wang. "Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing." Applied Sciences 13, no. 13 (2023): 7773. http://dx.doi.org/10.3390/app13137773.

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Highly deceptive deepfake technologies have caused much controversy, e.g., artificial intelligence-based software can automatically generate nude photos and deepfake images of anyone. This brings considerable threats to both individuals and society. In addition to video and image forgery, audio forgery poses many hazards but lacks sufficient attention. Furthermore, existing works have only focused on voice spoof detection, neglecting the identification of spoof algorithms. It is of great value to recognize the algorithm for synthesizing spoofing voices in traceability. This study presents a sy

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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 b

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20

Suman, Saurabh, and Dr Nagesh Salimath. "Designing Intuitive and Effective Dynamic Facial Authentication: Machine Interaction with Human Factors." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1642–46. http://dx.doi.org/10.22214/ijraset.2023.54946.

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Abstract: The objective of this paper is to propose a spoof-free Face Liveness Detection system with an active approach that uses the challenge-response methodology which detects the motions and gestures of the user, thereby analyzing and recognizing a real face from a photo. With cybercrimes on the rise each day, identity thefts being one of them- especially in an unsupervised authentication system, has given rise to serious security concerns. Face liveness detection assures the user's actual presence and validates their identities. Along with that, it prevents fraudulent reproduction of face

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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 phot

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Patil, Pooja R., and Subhash S. Kulkarni. "Survey of non-intrusive face spoof detection methods." Multimedia Tools and Applications 80, no. 10 (2021): 14693–721. http://dx.doi.org/10.1007/s11042-020-10338-1.

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Alshareef, Norah, Xiaohong Yuan, Kaushik Roy, and Mustafa Atay. "A Study of Gender Bias in Face Presentation Attack and Its Mitigation." Future Internet 13, no. 9 (2021): 234. http://dx.doi.org/10.3390/fi13090234.

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In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fair

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Li, Kaicheng, Hongyu Yang, Binghui Chen, Pengyu Li, Biao Wang, and Di Huang. "Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 1351–59. http://dx.doi.org/10.1609/aaai.v37i1.25219.

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Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof t

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N, RAMYA. "Liveness Detector for Face Recognition System Fake Vs Real." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47812.

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Abstract - facial recognition systems are increasingly deployed for identity verification and security, but they remain vulnerable to spoofing attacks using photographs, videos, or 3D masks. To address these challenges, a liveness detection mechanism is critical to distinguish between real, live human faces and spoofed or fake inputs. This paper presents a liveness detection framework integrated with a facial recognition system, utilizing techniques such as eye-blink detection, facial micro- movements, texture analysis, and 3D depth estimation. The proposed system aims to enhance the security

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Parveen, Dr Sajida, Sharifah Mumtazah Syed Ahmad, Nadeem Naeem Bhatti, Imtiaz Ali Halepoto, and Shamashad Lakho. "Robust Hybrid Texture Descriptor (HTD) and a parallel score based fusion for face verification and liveness detection system." VFAST Transactions on Software Engineering 12, no. 2 (2024): 85–94. http://dx.doi.org/10.21015/vtse.v12i2.1828.

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Currently, most of the biometric recognition systems are based on face verification are susceptible to the spoof attacks. Video replays, printed photographs and 3D mask attacks provoke false acceptance lest some necessary counter-measures are employed. We focus on still face spoof attacks considered as one of the most easily generated attacks and challenging for modern face verification systems. To detect face spoofing, most of existing countermeasures focus on designing discriminative features to analyze the textural properties of facial skin. To improve the texture discriminating properties

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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 meth

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Fang, Meiling, Naser Damer, Florian Kirchbuchner, and Arjan Kuijper. "Real masks and spoof faces: On the masked face presentation attack detection." Pattern Recognition 123 (March 2022): 108398. http://dx.doi.org/10.1016/j.patcog.2021.108398.

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Fei, Jianwei, Chengsheng Yuan, Qiang Zhang, Zhihua Xia, and Fei Gu. "Face spoof detection using feature map superposition and CNN." International Journal of Computational Science and Engineering 22, no. 2/3 (2020): 355. http://dx.doi.org/10.1504/ijcse.2020.10029396.

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Gu, Fei, Zhihua Xia, Jianwei Fei, Chengsheng Yuan, and Qiang Zhang. "Face spoof detection using feature map superposition and CNN." International Journal of Computational Science and Engineering 22, no. 2/3 (2020): 355. http://dx.doi.org/10.1504/ijcse.2020.107356.

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Pandey, Akhilesh Kumar, and Rajoo Pandey. "FACE-SPOOF DETECTION USING RADON TRANSFORM BASED STATISTICAL MEASURES." ICTACT Journal on Image and Video Processing 10, no. 4 (2020): 2177–81. https://doi.org/10.21917/ijivp.2020.0311.

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With the rising popularity of biometric traits-based authentication systems, their weaknesses are also grabbing attention of the research communities. This paper introduces a new anti-spoofing scheme for face recognition systems which exploits different measures based on the radon transform. The feature set used in the proposed method consists of five popularly known statistical moments, and uses support vector machine for classification. Extensive simulations are carried out using two different databases to assess the performance of the proposed method. It is found that the proposed method ac

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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 tru

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Muthanna Shibel, Ahmed, Sharifah Mumtazah Syed Ahmad, Luqman Hakim Musa, and Mohammed Nawfal Yahya. "DEEP LEARNING DETECTION OF FACIAL BIOMETRIC PRESENTATION ATTACK." LIFE: International Journal of Health and Life-Sciences 8 (October 23, 2023): 61–78. http://dx.doi.org/10.20319/lijhls.2022.8.6178.

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Face recognition systems have gained increasing importance in today’s society, which applications range from access controls to secure systems to electronic devices such as mobile phones and laptops. However, the security of face recognition systems is currently being threatened by the emergence of spoofing attacks that happens when someone tries to unauthorizedly bypass the biometric system by presenting a photo, 3-dimensional mask, or replay video of a legit user. The video attacks are perhaps one of the most frequent, cheapest, and simplest spoofing techniques to cheat face recognition syst

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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 moda

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Achmad, Bimo Vallentino, and Supatman Supatman. "FACIAL PHOTO AUTHENTICITY DETECTION USING FACE RECOGNITION AND LIVENESS DETECTION." Jurnal Teknik Informatika (Jutif) 5, no. 5 (2024): 1423–32. https://doi.org/10.52436/1.jutif.2024.5.5.2328.

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Facial recognition has been widely adopted by many systems as authentication. However, relying on facial photos for authentication is insufficient, as these can be manipulated using printed or digital photos. One method that can be used to prevent this is to integrate face recognition with liveness detection. In this research, face recognition and liveness detection are implemented using a Convolutional Neural Network (CNN) because CNN has the ability to process and extract features from photos effectively. There are two types of datasets used, namely CelebA-Spoof for liveness detection and lf

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Kusuma, Indra Bayu, Arida Kartika, Tjokorda Agung Budi W, Kurniawan Nur Ramadhani, and Febryanti Sthevanie. "Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance." International Journal on Information and Communication Technology (IJoICT) 4, no. 2 (2019): 11. http://dx.doi.org/10.21108/ijoict.2018.42.134.

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Particularly in the field of biometric security using human face has been widely implemented in the real world. Currently the human face is one of the guidelines in the security system. Nowadays the challenge is how to detect data falsification; such an attack is called spoofing. Spoofing occurs when someone is trying to pretend to be someone else by falsifying the original data and then that person may gain illegal access and benefit him. For example one can falsify the face recognition system using photographs, video, masks or 3D models. In this paper image spoofing human face detection usin

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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 Histog

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Liang, Yuxin, Chaoqun Hong, and Weiwei Zhuang. "Face Spoof Attack Detection with Hypergraph Capsule Convolutional Neural Networks." International Journal of Computational Intelligence Systems 14, no. 1 (2021): 1396. http://dx.doi.org/10.2991/ijcis.d.210419.003.

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Prof, Hadadi Sudheendra, and N. Krishnamurthy Dr. "Novel Promising Algorithm to suppress Spoof Attack by Cryptography Firewall2014." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 102–9. https://doi.org/10.31142/ijtsrd15801.

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Spoof attack suppression by the biometric information incorporation is the new and modern method ofavoid and as well suppression the attack online as well Off line . Wireless networks provide variousadvantages in real world. This can help businesses to increase their productivity, lower cost andeffectiveness, increase scalability and improve relationship with business partners and attractcustomers. In recent decades, we have witnessed the evolution of biometric technology from the firstpioneering works in face and voice recognition to the current state of development wherein a widespectrum of

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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

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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.

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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 m

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Dwivedi, Abhishek, and Shekhar Verma. "SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept." Scientific Temper 13, no. 02 (2022): 165–72. http://dx.doi.org/10.58414/scientifictemper.2022.13.2.25.

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Face spoofing refers to “tricking” a facial recognition system to gain unauthorized access to aparticular system. It is mostly used to steal data and money or spread malware. The maliciousimpersonation of oneself is a critical component of face spoofing to gain access to a system.It is observed in many identity theft cases, particularly in the financial sector. In 2015, Wen etal. presented experimental results for cutting-edge commercial off-the-shelf face recognitionsystems. These demonstrated the probability of fake face images being accepted as genuine.The probability could be as high as 70

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Anand, Diksha. "Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (2018): 1499–509. http://dx.doi.org/10.22214/ijraset.2018.6220.

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Huszár, Viktor Dénes, and Vamsi Kiran Adhikarla. "Live Spoofing Detection for Automatic Human Activity Recognition Applications." Sensors 21, no. 21 (2021): 7339. http://dx.doi.org/10.3390/s21217339.

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Human Activity Recognition (HAR) has become increasingly crucial in several applications, ranging from motion-driven virtual games to automated video surveillance systems. In these applications, sensors such as smart phone cameras, web cameras or CCTV cameras are used for detecting and tracking physical activities of users. Inevitably, spoof detection in HAR is essential to prevent anomalies and false alarms. To this end, we propose a deep learning based approach that can be used to detect spoofing in various fields such as border control, institutional security and public safety by surveillan

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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.

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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 d

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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 net

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Dwivedi, Abhishek, and Shekhar Verma. ""SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept"." SCIENTIFIC TEMPER 13, no. 2 (2022): 165–72. http://dx.doi.org/10.58414/scientifictemper.13.2.2022.165-172.

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Dwivedi, Abhishek, and Shekhar Verma. ""SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept"." SCIENTIFIC TEMPER 13, no. 2 (2022): 166–73. http://dx.doi.org/10.58414/scientifictemper.13.2.2022.166-173.

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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 li

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