Journal articles: 'Liveness detection'

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Marcialis, Gian Luca, and Fabio Roli. "Liveness detection competition 2009." Biometric Technology Today 17, no. 3 (March 2009): 7–9. http://dx.doi.org/10.1016/s0969-4765(09)70038-4.

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Xin, Yang, Yi Liu, Zhi Liu, Xuemei Zhu, Lingshuang Kong, Dongmei Wei, Wei Jiang, and Jun Chang. "A survey of liveness detection methods for face biometric systems." Sensor Review 37, no. 3 (June 19, 2017): 346–56. http://dx.doi.org/10.1108/sr-08-2015-0136.

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Purpose Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches. Design/methodology/approach This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures. Originality/value This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

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Rani, Rajneesh, and Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach." International Journal of Intelligent Information Technologies 17, no. 1 (January 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.

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In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

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Drahansky, Martin, Michal Dolezel, Jan Vana, Eva Brezinova, Jaegeol Yim, and Kyubark Shim. "New Optical Methods for Liveness Detection on Fingers." BioMed Research International 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/197925.

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This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities—the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection.

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Wu, Lifang, Yaowen Xu, Meng Jian, Xiao Xu, and Wei Qi. "Face liveness detection scheme with static and dynamic features." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (March 2018): 1840001. http://dx.doi.org/10.1142/s0219691318400015.

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Face liveness detection is a significant research topic in face-based online authentication. The current face liveness detection approaches utilize either static or dynamic features, but not both. In fact, the dynamic and static features have different advantages in face liveness detection. In this paper, we propose a scheme combining dynamic and static features to capture merits of them for face liveness detection. First, the dynamic maps are captured from the inter-frame motion in the video, which investigates motion information of the face in the video. Then, with a Convolutional Neural Network (CNN), the dynamic and static features are extracted from the dynamic maps and the frame images, respectively. Next, in CNN, the fully connected layers containing the dynamic and static features are concatenated to form a fused feature. Finally, the fused features are used to train a binary Support Vector Machine (SVM) classifier, which classifies the frames into two categories, i.e. frame with real or fake face. Experimental results and the corresponding analysis demonstrate that the proposed scheme is capable of discovering face liveness by fusing dynamic and static features and it outperforms the current state-of-the-art face liveness detection approaches.

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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 (December 1, 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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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 (February 17, 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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Singh, Manminder, and A. S. Arora. "A Novel Face Liveness Detection Algorithm with Multiple Liveness Indicators." Wireless Personal Communications 100, no. 4 (April 6, 2018): 1677–87. http://dx.doi.org/10.1007/s11277-018-5661-1.

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Moon, Y. S., J. S. Chen, K. C. Chan, K. So, and K. C. Woo. "Wavelet based fingerprint liveness detection." Electronics Letters 41, no. 20 (2005): 1112. http://dx.doi.org/10.1049/el:20052577.

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Kim, Sooyeon, Yuseok Ban, and Sangyoun Lee. "Face Liveness Detection Using Defocus." Sensors 15, no. 1 (January 14, 2015): 1537–63. http://dx.doi.org/10.3390/s150101537.

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Ali, Asad, Sanaul Hoque, and Farzin Deravi. "Gaze stability for liveness detection." Pattern Analysis and Applications 21, no. 2 (November 10, 2016): 437–49. http://dx.doi.org/10.1007/s10044-016-0587-2.

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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 (April 24, 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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Koshy, Ranjana, and Ausif Mahmood. "Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences." Entropy 22, no. 10 (October 21, 2020): 1186. http://dx.doi.org/10.3390/e22101186.

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Face liveness detection is a critical preprocessing step in face recognition for avoiding face spoofing attacks, where an impostor can impersonate a valid user for authentication. While considerable research has been recently done in improving the accuracy of face liveness detection, the best current approaches use a two-step process of first applying non-linear anisotropic diffusion to the incoming image and then using a deep network for final liveness decision. Such an approach is not viable for real-time face liveness detection. We develop two end-to-end real-time solutions where nonlinear anisotropic diffusion based on an additive operator splitting scheme is first applied to an incoming static image, which enhances the edges and surface texture, and preserves the boundary locations in the real image. The diffused image is then forwarded to a pre-trained Specialized Convolutional Neural Network (SCNN) and the Inception network version 4, which identify the complex and deep features for face liveness classification. We evaluate the performance of our integrated approach using the SCNN and Inception v4 on the Replay-Attack dataset and Replay-Mobile dataset. The entire architecture is created in such a manner that, once trained, the face liveness detection can be accomplished in real-time. We achieve promising results of 96.03% and 96.21% face liveness detection accuracy with the SCNN, and 94.77% and 95.53% accuracy with the Inception v4, on the Replay-Attack, and Replay-Mobile datasets, respectively. We also develop a novel deep architecture for face liveness detection on video frames that uses the diffusion of images followed by a deep Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to classify the video sequence as real or fake. Even though the use of CNN followed by LSTM is not new, combining it with diffusion (that has proven to be the best approach for single image liveness detection) is novel. Performance evaluation of our architecture on the REPLAY-ATTACK dataset gave 98.71% test accuracy and 2.77% Half Total Error Rate (HTER), and on the REPLAY-MOBILE dataset gave 95.41% accuracy and 5.28% HTER.

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Liu, Shuhua, Yu Song, Mengyu Zhang, Jianwei Zhao, Shihao Yang, and Kun Hou. "An Identity Authentication Method Combining Liveness Detection and Face Recognition." Sensors 19, no. 21 (October 31, 2019): 4733. http://dx.doi.org/10.3390/s19214733.

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In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. We combined the liveness detection and FaceNet model for identity authentication. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication.

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Koshy, Ranjana, and Ausif Mahmood. "Optimizing Deep CNN Architectures for Face Liveness Detection." Entropy 21, no. 4 (April 20, 2019): 423. http://dx.doi.org/10.3390/e21040423.

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Face recognition is a popular and efficient form of biometric authentication used in many software applications. One drawback of this technique is that it is prone to face spoofing attacks, where an impostor can gain access to the system by presenting a photograph of a valid user to the sensor. Thus, face liveness detection is a necessary step before granting authentication to the user. In this paper, we have developed deep architectures for face liveness detection that use a combination of texture analysis and a convolutional neural network (CNN) to classify the captured image as real or fake. Our development greatly improved upon a recent approach that applies nonlinear diffusion based on an additive operator splitting scheme and a tridiagonal matrix block-solver algorithm to the image, which enhances the edges and surface texture in the real image. We then fed the diffused image to a deep CNN to identify the complex and deep features for classification. We obtained 100% accuracy on the NUAA Photograph Impostor dataset for face liveness detection using one of our enhanced architectures. Further, we gained insight into the enhancement of the face liveness detection architecture by evaluating three different deep architectures, which included deep CNN, residual network, and the inception network version 4. We evaluated the performance of each of these architectures on the NUAA dataset and present here the experimental results showing under what conditions an architecture would be better suited for face liveness detection. While the residual network gave us competitive results, the inception network version 4 produced the optimal accuracy of 100% in liveness detection (with nonlinear anisotropic diffused images with a smoothness parameter of 15). Our approach outperformed all current state-of-the-art methods.

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Wei, Yang, Ivy Kim D. Machica, Cristina E. Dumdumaya, Jan Carlo T. Arroyo, and AllemarJhone P. Delima. "Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (August 2, 2022): 45–53. http://dx.doi.org/10.46338/ijetae0822_06.

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—Face liveness detection is an important biometric authentication method for face recognition securitythat is used to determine a fake face from an authentic one. In this paper, a liveness detection method based on optimized LeNet5 is proposed. The LeNet-5 is optimized by increasing the convolution kerneland byintroducing a global average pooling. The simulation results show that the proposed model obtained the highest recognition rate of 99.95% as against the 96.67% and 98.23% accuracy from the Support Vector Machine (SVM) and LeNet-5 models, respectively.The results denote that the proposed model has a high recognition rate in face liveness detection

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Jiang, Yujia, and Xin Liu. "Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid." Journal of Electrical and Computer Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1539298.

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Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.

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Chakraborty, Saptarshi, and Dhrubajyoti Das. "An Overview of Face Liveness Detection." International Journal on Information Theory 3, no. 2 (April 30, 2014): 11–25. http://dx.doi.org/10.5121/ijit.2014.3202.

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Yuan, Chengsheng, Mingyu Chen, and Ying Lv. "Fingerprint Liveness Detection Approaches: A SURVEY." International Journal of Autonomous and Adaptive Communications Systems 17, no. 3 (2024): 1. http://dx.doi.org/10.1504/ijaacs.2024.10046755.

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Czajka, Adam. "Pupil Dynamics for Iris Liveness Detection." IEEE Transactions on Information Forensics and Security 10, no. 4 (April 2015): 726–35. http://dx.doi.org/10.1109/tifs.2015.2398815.

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Hu, Yang, Konstantinos Sirlantzis, and Gareth Howells. "Iris liveness detection using regional features." Pattern Recognition Letters 82 (October 2016): 242–50. http://dx.doi.org/10.1016/j.patrec.2015.10.010.

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Rehman, Yasar Abbas Ur, Lai Man Po, and Mengyang Liu. "LiveNet: Improving features generalization for face liveness detection using convolution neural networks." Expert Systems with Applications 108 (October 2018): 159–69. http://dx.doi.org/10.1016/j.eswa.2018.05.004.

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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 (July 30, 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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Zolotarev, V. V., А. О. Povazhnyuk, and Е. А. Maro. "METHODS OF IMPROVED USER IDENTIFICATION BASED ON LIVENESS DETECTION TECHNOLOGY." IZVESTIYA SFedU. ENGINEERING SCIENCES, no. 2 (May 31, 2022): 212–25. http://dx.doi.org/10.18522/2311-3103-2022-2-212-225.

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NIKAM, SHANKAR BHAUSAHEB, and SUNEETA AGARWAL. "CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION." International Journal of Image and Graphics 09, no. 02 (April 2009): 171–99. http://dx.doi.org/10.1142/s0219467809003393.

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Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

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Babikir Adam, Edriss Eisa, and Sathesh. "Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View." Journal of ISMAC 3, no. 1 (March 1, 2021): 16–30. http://dx.doi.org/10.36548/jismac.2021.1.002.

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Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.

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Ebrahimpour, Nader, Mustafa Arda Ayden, and Banu Altay. "Liveness control in face recognition with deep learning methods." European Journal of Research and Development 2, no. 2 (June 7, 2022): 92–101. http://dx.doi.org/10.56038/ejrnd.v2i2.36.

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Today, automatic identification of individuals from biometric features is widely used in identification and authentication, security, and monitoring applications. Since facial recognition is a more user-friendly and comfortable method than other biometric methods, it has grown rapidly in recent years. However, most facial recognition systems are vulnerable to spoofing attacks. Therefore, face liveness detection (FLD) methods are of great importance. On the other hand, unlike traditional methods, deep learning techniques promise to significantly increase the accuracy of facial liveness detection systems and eliminate the difficulties of the real-world implementation of these systems. Therefore, in this paper, the application of some deep learning models to detect face liveness is reviewed and compared with each other.

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Singh, Avinash Kumar, Piyush Joshi, and G. C. Nandi. "Face liveness detection through face structure analysis." International Journal of Applied Pattern Recognition 1, no. 4 (2014): 338. http://dx.doi.org/10.1504/ijapr.2014.068327.

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Nogueira, Rodrigo Frassetto, Roberto de Alencar Lotufo, and Rubens Campos Machado. "Fingerprint Liveness Detection Using Convolutional Neural Networks." IEEE Transactions on Information Forensics and Security 11, no. 6 (June 2016): 1206–13. http://dx.doi.org/10.1109/tifs.2016.2520880.

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Thavalengal, Shejin, Tudor Nedelcu, Petronel Bigioi, and Peter Corcoran. "Iris liveness detection for next generation smartphones." IEEE Transactions on Consumer Electronics 62, no. 2 (May 2016): 95–102. http://dx.doi.org/10.1109/tce.2016.7514667.

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Kim, Wonjun. "Fingerprint Liveness Detection Using Local Coherence Patterns." IEEE Signal Processing Letters 24, no. 1 (January 2017): 51–55. http://dx.doi.org/10.1109/lsp.2016.2636158.

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Akhtar, Zahid, Christian Micheloni, and Gian Luca Foresti. "Biometric Liveness Detection: Challenges and Research Opportunities." IEEE Security & Privacy 13, no. 5 (September 2015): 63–72. http://dx.doi.org/10.1109/msp.2015.116.

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Sharma, Ram Prakash, and Somnath Dey. "Fingerprint liveness detection using local quality features." Visual Computer 35, no. 10 (December 15, 2018): 1393–410. http://dx.doi.org/10.1007/s00371-018-01618-x.

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Wang, Yiding, Di Zhang, and Qi Qi. "Liveness detection for dorsal hand vein recognition." Personal and Ubiquitous Computing 20, no. 3 (May 6, 2016): 447–55. http://dx.doi.org/10.1007/s00779-016-0922-z.

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Li, Xin, Wei Wu, Tao Li, Yang Su, and Lilin Yang. "Face Liveness Detection Based on Parallel CNN." Journal of Physics: Conference Series 1549 (June 2020): 042069. http://dx.doi.org/10.1088/1742-6596/1549/4/042069.

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Pallavi, Gautam, and Jayash Kumar. "Face Liveness Detection using Local Diffused Patterns." International Journal of Computer Applications 149, no. 4 (September 15, 2016): 1–5. http://dx.doi.org/10.5120/ijca2016911380.

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Ghiani, Luca, Abdenour Hadid, Gian Luca Marcialis, and Fabio Roli. "Fingerprint liveness detection using local texture features." IET Biometrics 6, no. 3 (January 6, 2017): 224–31. http://dx.doi.org/10.1049/iet-bmt.2016.0007.

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Kollreider, K., H. Fronthaler, and J. Bigun. "Non-intrusive liveness detection by face images." Image and Vision Computing 27, no. 3 (February 2009): 233–44. http://dx.doi.org/10.1016/j.imavis.2007.05.004.

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Seo, Jongwoo, and In-Jeong Chung. "Face Liveness Detection Using Thermal Face-CNN with External Knowledge." Symmetry 11, no. 3 (March 10, 2019): 360. http://dx.doi.org/10.3390/sym11030360.

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Face liveness detection is important for ensuring security. However, because faces are shown in photographs or on a display, it is difficult to detect the real face using the features of the face shape. In this paper, we propose a thermal face-convolutional neural network (Thermal Face-CNN) that knows the external knowledge regarding the fact that the real face temperature of the real person is 36~37 degrees on average. First, we compared the red, green, and blue (RGB) image with the thermal image to identify the data suitable for face liveness detection using a multi-layer neural network (MLP), convolutional neural network (CNN), and C-support vector machine (C-SVM). Next, we compared the performance of the algorithms and the newly proposed Thermal Face-CNN in a thermal image dataset. The experiment results show that the thermal image is more suitable than the RGB image for face liveness detection. Further, we also found that Thermal Face-CNN performs better than CNN, MLP, and C-SVM when the precision is slightly more crucial than recall through F-measure.

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Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (June 30, 2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.

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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef

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Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad Muzzamil Luqman, and Jean-Christophe Burie. "Document Liveness Challenge Dataset (DLC-2021)." Journal of Imaging 8, no. 7 (June 28, 2022): 181. http://dx.doi.org/10.3390/jimaging8070181.

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Various government and commercial services, including, but not limited to, e-government, fintech, banking, and sharing economy services, widely use smartphones to simplify service access and user authorization. Many organizations involved in these areas use identity document analysis systems in order to improve user personal-data-input processes. The tasks of such systems are not only ID document data recognition and extraction but also fraud prevention by detecting document forgery or by checking whether the document is genuine. Modern systems of this kind are often expected to operate in unconstrained environments. A significant amount of research has been published on the topic of mobile ID document analysis, but the main difficulty for such research is the lack of public datasets due to the fact that the subject is protected by security requirements. In this paper, we present the DLC-2021 dataset, which consists of 1424 video clips captured in a wide range of real-world conditions, focused on tasks relating to ID document forensics. The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. For the presented dataset, benchmark baselines are provided for tasks such as screen recapture detection and glare detection. The data presented are openly available in Zenodo.

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Lee, Youn Kyu, Jongwook Jeong, and Dongwoo Kang. "An Effective Orchestration for Fingerprint Presentation Attack Detection." Electronics 11, no. 16 (August 11, 2022): 2515. http://dx.doi.org/10.3390/electronics11162515.

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Fingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a number of techniques have focused on fingerprint presentation attack detection, they suffer from inaccuracy in determining the liveness of fingerprints and performance degradation on unknown types of fingerprints. To address existing limitations, we present a robust fingerprint presentation attack detection method that orchestrates different types of neural networks by incorporating a triangular normalization method. Our method has been evaluated on a public benchmark comprising 13,000 images with five different fake materials. The evaluation exhibited our method’s higher accuracy in determining the liveness of fingerprints as well as better generalization performance on different types of fingerprints compared to existing techniques.

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F.W. Onifade, Olufade, Paul Akinde, and Folasade Olubusola Isinkaye. "Circular Gabor wavelet algorithm for fingerprint liveness detection." Journal of Advanced Computer Science & Technology 9, no. 1 (January 11, 2020): 1. http://dx.doi.org/10.14419/jacst.v9i1.29908.

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Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.

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Khade, Smita, Shilpa Gite, and Biswajeet Pradhan. "Iris Liveness Detection Using Multiple Deep Convolution Networks." Big Data and Cognitive Computing 6, no. 2 (June 15, 2022): 67. http://dx.doi.org/10.3390/bdcc6020067.

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In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained networks, including VGG-16, Inceptionv3, Resnet50, Densenet121, and EfficientNetB7, to recognize iris liveness using transfer learning techniques. These models are compared using three state-of-the-art biometric databases: the LivDet-Iris 2015 dataset, IIITD contact dataset, and ND Iris3D 2020 dataset. Validation accuracy, loss, precision, recall, and f1-score, APCER (attack presentation classification error rate), NPCER (normal presentation classification error rate), and ACER (average classification error rate) were used to evaluate the performance of all pre-trained models. According to the observational data, these models have a considerable ability to transfer their experience to the field of iris recognition and to recognize the nanostructures within the iris region. Using the ND Iris 3D 2020 dataset, the EfficeintNetB7 model has achieved 99.97% identification accuracy. Experiments show that pre-trained models outperform other current iris biometrics variants.

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Li, Jing, Yajun Chen, and Erhu Zhang. "Comprehensive edge direction descriptor for fingerprint liveness detection." Signal Processing: Image Communication 102 (March 2022): 116603. http://dx.doi.org/10.1016/j.image.2021.116603.

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Marcialis, Gian Luca, Pietro Coli, and Fabio Roli. "Fingerprint Liveness Detection Based on Fake Finger Characteristics." International Journal of Digital Crime and Forensics 4, no. 3 (July 2012): 1–19. http://dx.doi.org/10.4018/jdcf.2012070101.

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The vitality detection of fingerprints is currently acknowledged as a serious issue for personal identity verification systems. This problem, raised some years ago, is related to the fact that the 3d shape pattern of a fingerprint can be reproduced using artificial materials. An image quite similar to that of true, alive, fingerprint, is derived if such “fake fingers” are submitted to an electronic scanner. Since introducing hardware dedicated to liveness detection in scanners is expensive, software-based solutions, based on image processing algorithms, have been proposed as alternative. So far, proposed approaches are based on features exploiting characteristics of a live finger (e.g., finger perspiration). Such features can be named live-based, or vitality-based features. In this paper, the authors propose and motivate the use of a novel kind of features exploiting characteristics noticed in the reproduction of fake fingers, that they named fake-based features. Then the authors propose a possibile implementation of this kind of features based on the power spectrum of the fingerprint image. The proposal is compared and integrated with several live-based features at the state-of-the-art, and shows very good liveness detection performances. Experiments are carried out on a data set much larger than commonly adopted ones, containing images from three different optical sensors.

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Chattopadhyay, Abir, Sumanta Das, and Ishita De Ghosh. "A liveness detection system for sclera biometric applications." International Journal of Biometrics 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbm.2023.10050762.

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Kim, W., and C. Jung. "Local accumulated smoothing patterns for fingerprint liveness detection." Electronics Letters 52, no. 23 (November 2016): 1912–14. http://dx.doi.org/10.1049/el.2016.3371.

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Feng, Litong, Lai-Man Po, Yuming Li, and Fang Yuan. "Face liveness detection using shearlet-based feature descriptors." Journal of Electronic Imaging 25, no. 4 (July 21, 2016): 043014. http://dx.doi.org/10.1117/1.jei.25.4.043014.

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Gragnaniello, Diego, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. "Local contrast phase descriptor for fingerprint liveness detection." Pattern Recognition 48, no. 4 (April 2015): 1050–58. http://dx.doi.org/10.1016/j.patcog.2014.05.021.

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Journal articles on the topic 'Liveness detection'

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