Optimizing Deep CNN Architectures for Face Liveness Detection

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.http://dx.doi.org/10.3390/e2104042

Enhanced Face Liveness Detection Based on Features From Nonlinear Diffusion Using Specialized Deep Convolution Network And Its Application In OAuth

The major contribution of this research is the development of enhanced algorithms that will prevent face spoofing attacks by utilizing a single image captured from a 2-D printed image or a recorded video. We first apply a nonlinear diffusion based on an additive operator splitting (AOS) scheme with a large time step to acquire a diffused image. The AOS-based scheme enables fast diffusion that successfully reveals the depth information and surface texture in the input image. Then a specialized deep convolution neural network is developed that can extract the discriminative and high-level features of the input diffused image to differentiate between a fake face and a real face. Our proposed method yields higher accuracy as compared to the previously implemented state-of-the-art methods. As an application of the face liveness detection, we develop face biometric authentication in an Open Authorization (OAuth) framework for controlling secure access to web resources. We implement a complete face verification system that consists of face liveness detection followed by face authentication that uses Local Binary Pattern as features for face recognition. The entire face authentication process consists of four services: an image registration service, a face liveness detection service, a verification service, and an access token service for use in OAuth

Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences

The major contribution of this research is the development of deep architectures for face liveness detection on a static image as well as video sequences that use a combination of texture analysis and deep Convolutional Neural Network (CNN) to classify the captured image or video as real or fake. 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 or recorded video of a valid user to the sensor. Thus, face liveness detection is a critical preprocessing step in face recognition authentication systems. The first part of our research was on face liveness detection on a static image, where we applied nonlinear diffusion based on an additive operator splitting scheme and a tri-diagonal matrix block-solver algorithm to the image, which enhances the edges and surface texture in the real image. The diffused image was then fed to a deep CNN to identify the complex and deep features for classification. We obtained high accuracy on the NUAA Photograph Impostor dataset using one of our enhanced architectures. In the second part of our research, we developed an end-to-end real-time solution for face liveness detection on static images, where instead of using a separate preprocessing step for diffusing the images, we used a combined architecture where the diffusion process and CNN were implemented in a single step. This integrated approach gave promising results with two different architectures, on the Replay-Attack and Replay-Mobile datasets. We also developed a novel deep architecture for face liveness detection on video frames that uses the diffusion of images followed by a deep CNN and Long Short-Term Memory (LSTM) to classify the video sequence as real or fake. Performance evaluation of our architecture on the Replay-Attack and Replay-Mobile datasets gave very competitive results. We performed liveness detection on video sequences using diffusion and the Two-Stream Inflated 3D ConvNet (I3D) architecture, and our experiments on the Replay-Attack and Replay-Mobile datasets gave very good results

Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences

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.https://doi.org/10.3390/e2210118

Assessment of the Viability of a Biometric Characteristic in the Context of Biometric Authentication on Mobile Devices

The issue of safe utilization of mobile devices is becoming an increasingly important problem, among others due to the widespread use of such devices to access sensitive data (such as electronic documents or banking data). In our work we analyze the use of biometric techniques in order to secure a mobile device, with particular emphasis on the viability of selected biometric characteristics. For this purpose, we investigate the possibility of applying machine learning models to assess the authenticity of a biometric characteristic. Results of our tests have shown that the most effective method of assessing the viability of a biometric characteristic involves blink and smile detection