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

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

Face Liveness Detection under Processed Image Attacks

Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques