Face Liveness Detection Method Research Articles

Liveness control in face recognition with deep learning methods

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.

SLNet: Stereo face liveness detection via dynamic disparity-maps and convolutional neural network

Current state-of-the-art dual camera-based face liveness detection methods utilize either hand-crafted features, such as disparity, or deep texture features to classify a live face and face Presentation Attack (PA). However, these approaches limit the effectiveness of classifiers, particularly deep Convolutional Neural Networks (CNN) to unknown face PA in adverse scenarios. In contrast to these approaches, in this paper, we show that supervising a deep CNN classifier by learning disparity features using the existing CNN layers improves the performance and robustness of CNN to unknown types of face PA. For this purpose, we propose to supervise a CNN classifier by introducing a disparity layer within CNN to learn the dynamic disparity-maps. Subsequently, the rest of the convolutional layers, following the disparity layer, in the CNN are supervised using the learned dynamic disparity-maps for face liveness detection. We further propose a new video-based stereo face anti-spoofing database with various face PA and different imaging qualities. Experiments on the proposed stereo face anti-spoofing database are performed using various test case scenarios. The experimental results indicate that our proposed system shows promising performance and has good generalization ability.

Diffusion-based kernel matrix model for face liveness detection

Face recognition and verification systems are vulnerable to video spoofing attacks. In this paper, we present a diffusion-based kernel matrix model for face liveness detection. We use the anisotropic diffusion to enhance the edges of each frame in a video, and the kernel matrix model to extract the video features which we call the diffusion kernel (DK) features. The DK features reflect the inner correlation of the face images in the video. We employ a generalized multiple kernel learning method to fuse the DK features and the deep features extracted from convolution neural networks to achieve better performance. Our experimental evaluation on two publicly available datasets shows that the proposed method outperforms the state-of-art face liveness detection methods.

Face Liveness Detection Using a Flash Against 2D Spoofing Attack

Face recognition technique has been widely applied to personal identification systems due to its satisfying performance. However, its security may be a crucial issue, since many studies have shown that face recognition systems may be vulnerable in an adversarial environment, in which an adversary can camouflage as a legitimate user in order to mislead the system. Although face liveness detection methods have been proposed to distinguish real and fake faces, they are either time-consuming, costly, or sensitive to noise and illumination. This paper proposes a face liveness detection method with flash against 2D spoofing attack. Flash not only can enhance the differentiation between legitimate and illegitimate users, but it also reduces the influence of environmental factors. Two images are taken from a subject, one with flash and another without flash. Four texture and 2D structure descriptors with low computational complexity are used to capture information of the two images in our model. Advantages of our method include low installation cost of flash and no user cooperation required. A data set of 50 subjects collected under different scenarios is used in the experiments to evaluate the proposed method. The experimental results indicate that the proposed model performs better than existing liveness detection methods in different environmental scenarios. This paper confirms that the use of flash successfully improves face liveness detection in terms of accuracy, robustness, and running time.

A survey of liveness detection methods for face biometric systems

PurposeBiometric 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/approachThis 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/valueThis 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.

Multispectral face liveness detection method based on gradient features

Face liveness detection aims to distinguish genuine faces from disguised faces. Most previous works under visible light focus on classification of genuine faces and planar photos or videos. To handle the three-dimensional (3-D) disguised faces, liveness detection based on multispectral images has been shown to be an effective choice. In this paper, a gradient-based multispectral method has been proposed for face liveness detection. Three feature vectors are developed to reduce the influence of varying illuminations. The reflectance-based feature achieves the best performance, which has a true positive rate of 98.3% and a true negative rate of 98.7%. The developed methods are also tested on individual bands to provide a clue for band selection in the imaging system. Preliminary results on different face orientations are also shown. The contributions of this paper are threefold. First, a gradient-based multispectral method has been proposed for liveness detection, which considers the reflectance properties of all the distinctive regions in a face. Second, three illumination-robust features are studied based on a dataset with two-dimensional planar photos, 3-D mannequins, and masks. Finally, the performance of the method on different spectral bands and face orientations is also shown in the evaluations.