Google Groups no longer supports new Usenet posts or subscriptions. Historical content remains viewable.
Dismiss
Face Liveness Detection Dataset Download
16 views
Skip to first unread message
Rosy Felde
Jan 3, 2024, 10:10:50 PM1/3/24
to
Abstract: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 3637 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.Keywords: face liveness detection; convolutional neural network; thermal image; external knowledge
face liveness detection dataset download
Download File https://t.co/hZFOlnJ9kf
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.
Alternatively, we recommend rating a face as found if the relative distance is equal to or less than 0.25, which corresponds to an accuracy of about half the width of an eye in the image. The detection rate can directly be calculated by dividing the number of correctly found faces by the total number of faces in the dataset. The results for the BioID face detection algorithms can be found in:
Amazon Rekognition Face Liveness verifies that only real users, not bad actors using spoofs, can access your services. Amazon Rekognition Face Liveness analyzes a short selfie video to detect spoofs presented to the camera, such as printed photos, digital photos, digital videos, or 3D masks, as well as spoofs that bypass the camera, such as pre-recorded or deepfake videos. Face Liveness is a fully managed feature that can be easily added to your React web, native iOS, and native Android applications running on most devices with a front-facing camera. No infrastructure management, hardware-specific implementation, or machine learning expertise is required. The feature automatically scales up or down in response to demand, and you only pay for the face liveness checks you perform.
The remainder of this paper is organized as follows. In Section 2, the related works are presented. In Section 3, our proposed approach is described in detail. Three benchmark face presentation attack datasets are introduced in Section 4. In Section 5, we provide comprehensive experimental results and analysis. Last but not least, concluding remarks are drawn in Section 6.
To address the challenge introduced by face presentation attacks, many presentation attack detection techniques have been proposed, which can be arbitrarily formulated into two categories: deep learning-based methods and hand-crafted feature-based methods. The specific overview is extended as follows.
Deep learning can achieve promising results in the field of computer vision, which is also very effective when tackling face presentation attack detection task. In [2], CNN is utilized to extract deep features, and SVM is employed instead of fully connected layers for classification. Atoum et al. [27] present a two-stream network architecture to learn patch-based and depth-based features, and the classification result is determined by the fusion scores of both two streams. Rather than merely extracting spatial feature, a 3D-CNN structure is proposed in [6] to exploit the spatial-temporal features, which can capture more visual cues that are indeed useful for face presentation attack detection task. Meanwhile, a domain generalization regularization approach is incorporated for further enhancing the model generalization ability. Previous deep learning-based face presentation attack detection approaches formulate the task as a binary classification problem. Liu et al. [28] emphasize the importance of auxiliary supervision. Specifically, a CNN-RNN architecture is proposed to utilize depth map information and rPPG (remote Photoplethysmography) signs, which can both exploit spoof patterns across spatial and temporal domains. In [29], an augmented dataset is collected in a specific image synthesis way, which can further improve the robustness of the model.
The methodologies in this category mainly rely on defining specific patterns in advance for extracting discriminative features. Given that face presentation attack samples tend to be static, motion analysis-based schemes are developed, such as eye blinking [10], mouth movement [11], and just holistic face region movement analysis [13]. In general, the biometric information can be successfully obtained by analyzing the optical flow in specific areas of the image. Although the motion-related cue-based methods perform well when dealing with print attack, they may fail to complete the task of replay attack detection, where the motion-related cue for presentation attack detection can be easily inferred. Besides, image quality also can be a vital measurement toward face presentation attack detection. Galbally et al. [15] propose to resolve presentation attacks by calculating prominent factors among 25 image quality metrics. Di et al. [16] introduce an image distortion analysis countermeasure by evaluating four presentation attack patterns: specular reflection caused by display device, image blurriness, chromatic distribution variation, and poor color diversity. However, due to heavy computation, these methods are not efficient enough. It is worth mentioning that although various hand-crafted feature-based methods are proposed, there is still a lack of effective preprocessing to further improve the performance of the detector.
In addition, the effectiveness of texture descriptors in resolving face presentation attack problems has been verified by some works. For instance, multiscale local binary pattern (MSLBP) descriptor is designed for face presentation attack detection in [17], and a novel facial texture representation is introduced by using the spatial and temporal extensions of the local binary pattern (LBP-TOP) [33]. Besides, it is worth noting that Boulkenafet et al. [21] present a novel and appealing face presentation attack countermeasure by using color texture features, based on the assumption that gray-scale images are often used to display illuminance information, while more helpful color information are discarded. In fact, the RGB image cannot completely separate the luminance and chrominance signals while color texture features can be well extracted from HSV and YCbCr spaces. It is well-known that print attacks utilize photos of legitimate users to fool the face recognition system, while replay attacks often utilize electronic device such as mobile or tablet. Due to the restriction of the limited color gamut, the fake faces presented on the display device often show color degradation.
The effectiveness of texture descriptors and color space features in resolving face presentation attack detection task are verified. However, the discriminative features are generally extracted from original pixels in spatial domain, which are more or less impacted by nuisance noise introduced during image capturing. Besides, the study of combining various texture features within different color spaces to achieve the optimal color texture features still remains open in this community. Additionally, to the best of our knowledge, one single classifier cannot always bring optimal prediction results, compared with the powerful ensemble classifier. In virtue of our theoretical and empirical analysis in this paper, those negative factors can lead to bad detection results when training samples are mismatched with testing samples. To address those challenges, dependent of residual image via DW-filtering, it is proposed to design a high efficient ensemble face presentation attack detector based on RCTR.
35fe9a5643
face liveness detection dataset download
Download File https://t.co/hZFOlnJ9kf
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.
Alternatively, we recommend rating a face as found if the relative distance is equal to or less than 0.25, which corresponds to an accuracy of about half the width of an eye in the image. The detection rate can directly be calculated by dividing the number of correctly found faces by the total number of faces in the dataset. The results for the BioID face detection algorithms can be found in:
Amazon Rekognition Face Liveness verifies that only real users, not bad actors using spoofs, can access your services. Amazon Rekognition Face Liveness analyzes a short selfie video to detect spoofs presented to the camera, such as printed photos, digital photos, digital videos, or 3D masks, as well as spoofs that bypass the camera, such as pre-recorded or deepfake videos. Face Liveness is a fully managed feature that can be easily added to your React web, native iOS, and native Android applications running on most devices with a front-facing camera. No infrastructure management, hardware-specific implementation, or machine learning expertise is required. The feature automatically scales up or down in response to demand, and you only pay for the face liveness checks you perform.
The remainder of this paper is organized as follows. In Section 2, the related works are presented. In Section 3, our proposed approach is described in detail. Three benchmark face presentation attack datasets are introduced in Section 4. In Section 5, we provide comprehensive experimental results and analysis. Last but not least, concluding remarks are drawn in Section 6.
To address the challenge introduced by face presentation attacks, many presentation attack detection techniques have been proposed, which can be arbitrarily formulated into two categories: deep learning-based methods and hand-crafted feature-based methods. The specific overview is extended as follows.
Deep learning can achieve promising results in the field of computer vision, which is also very effective when tackling face presentation attack detection task. In [2], CNN is utilized to extract deep features, and SVM is employed instead of fully connected layers for classification. Atoum et al. [27] present a two-stream network architecture to learn patch-based and depth-based features, and the classification result is determined by the fusion scores of both two streams. Rather than merely extracting spatial feature, a 3D-CNN structure is proposed in [6] to exploit the spatial-temporal features, which can capture more visual cues that are indeed useful for face presentation attack detection task. Meanwhile, a domain generalization regularization approach is incorporated for further enhancing the model generalization ability. Previous deep learning-based face presentation attack detection approaches formulate the task as a binary classification problem. Liu et al. [28] emphasize the importance of auxiliary supervision. Specifically, a CNN-RNN architecture is proposed to utilize depth map information and rPPG (remote Photoplethysmography) signs, which can both exploit spoof patterns across spatial and temporal domains. In [29], an augmented dataset is collected in a specific image synthesis way, which can further improve the robustness of the model.
The methodologies in this category mainly rely on defining specific patterns in advance for extracting discriminative features. Given that face presentation attack samples tend to be static, motion analysis-based schemes are developed, such as eye blinking [10], mouth movement [11], and just holistic face region movement analysis [13]. In general, the biometric information can be successfully obtained by analyzing the optical flow in specific areas of the image. Although the motion-related cue-based methods perform well when dealing with print attack, they may fail to complete the task of replay attack detection, where the motion-related cue for presentation attack detection can be easily inferred. Besides, image quality also can be a vital measurement toward face presentation attack detection. Galbally et al. [15] propose to resolve presentation attacks by calculating prominent factors among 25 image quality metrics. Di et al. [16] introduce an image distortion analysis countermeasure by evaluating four presentation attack patterns: specular reflection caused by display device, image blurriness, chromatic distribution variation, and poor color diversity. However, due to heavy computation, these methods are not efficient enough. It is worth mentioning that although various hand-crafted feature-based methods are proposed, there is still a lack of effective preprocessing to further improve the performance of the detector.
In addition, the effectiveness of texture descriptors in resolving face presentation attack problems has been verified by some works. For instance, multiscale local binary pattern (MSLBP) descriptor is designed for face presentation attack detection in [17], and a novel facial texture representation is introduced by using the spatial and temporal extensions of the local binary pattern (LBP-TOP) [33]. Besides, it is worth noting that Boulkenafet et al. [21] present a novel and appealing face presentation attack countermeasure by using color texture features, based on the assumption that gray-scale images are often used to display illuminance information, while more helpful color information are discarded. In fact, the RGB image cannot completely separate the luminance and chrominance signals while color texture features can be well extracted from HSV and YCbCr spaces. It is well-known that print attacks utilize photos of legitimate users to fool the face recognition system, while replay attacks often utilize electronic device such as mobile or tablet. Due to the restriction of the limited color gamut, the fake faces presented on the display device often show color degradation.
The effectiveness of texture descriptors and color space features in resolving face presentation attack detection task are verified. However, the discriminative features are generally extracted from original pixels in spatial domain, which are more or less impacted by nuisance noise introduced during image capturing. Besides, the study of combining various texture features within different color spaces to achieve the optimal color texture features still remains open in this community. Additionally, to the best of our knowledge, one single classifier cannot always bring optimal prediction results, compared with the powerful ensemble classifier. In virtue of our theoretical and empirical analysis in this paper, those negative factors can lead to bad detection results when training samples are mismatched with testing samples. To address those challenges, dependent of residual image via DW-filtering, it is proposed to design a high efficient ensemble face presentation attack detector based on RCTR.
35fe9a5643
0 new messages