Face Spoofing Research Articles

IoT Cloud‐Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model

A face‐based authentication system has become an important topic in various fields of IoT applications such as identity validation for social care, crime detection, ATM access, computer security, etc. However, these authentication systems are vulnerable to different attacks. Presentation attacks have become a clear threat for facial biometric‐based authentication and security applications. To address this issue, we proposed a deep learning approach for face spoofing detection systems in IoT cloud‐based environment. The deep learning approach extracted features from multicolor space to obtain more information from the input face image regarding luminance and chrominance data. These features are combined and selected by the Minimum Redundancy Maximum Relevance (mRMR) algorithm to provide an efficient and discriminate feature set. Finally, the extracted deep color‐based features of the face image are used for face spoofing detection in a cloud environment. The proposed method achieves stable results with less training data compared to conventional deep learning methods. This advantage of the proposed approach reduces the time of processing in the training phase and optimizes resource management in storing training data on the cloud. The proposed system was tested and evaluated based on two challenging public access face spoofing databases, namely, Replay‐Attack and ROSE‐Youtu. The experimental results based on these databases showed that the proposed method achieved satisfactory results compared to the state‐of‐the‐art methods based on an equal error rate (EER) of 0.2% and 3.8%, respectively, for the Replay‐Attack and ROSE‐Youtu databases.

Progressive Transfer Learning for Face Anti-Spoofing.

Face anti-spoofing (FAS) techniques play an important role in defending face recognition systems against spoofing attacks. Existing FAS methods often require a large number of annotated spoofing face data to train effective anti-spoofing models. Considering the attacking nature of spoofing data and its diverse variants, obtaining all the spoofing types in advance is difficult. This would limit the performance of FAS networks in practice. Thus, an online learning FAS method is highly desirable. In this paper, we present a semi-supervised learning based framework to tackle face spoofing attacks with only a few labeled training data (e.g., ∼ 50 face images). Specifically, we progressively adopt the unlabeled data with reliable pseudo labels during training to enrich the variety of training data. We observed that face spoofing data are naturally presented in the format of video streams. Thus, we exploit the temporal consistency to consolidate the reliability of a pseudo label for a selected image. Furthermore, we propose an adaptive transfer mechanism to ameliorate the influence of unseen spoofing data. Benefiting from the progressively-labeling nature of our method, we are able to train our network on not only data of seen spoofing types (i.e., the source domain) but also unlabeled data of unseen attacking types (i.e., the target domain). In this way, our method can reduce the domain gap and is more practical in real-world anti-spoofing scenarios. Extensive experiments in both the intra-database and inter-database scenarios demonstrate that our method is on par with the state-of-the-art methods but employs remarkably less labeled data (less than 0.1% labeled spoofing data in a dataset). Moreover, our method significantly outperforms fully-supervised methods on cross-domain testing scenarios with the help of our progressive learning fashion.

Face spoofing detection using improved SegNet architecture with a blur estimation technique

Face spoofing detection using improved SegNet architecture with a blur estimation technique

Camera Invariant Feature Learning for Generalized Face Anti-Spoofing

There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.

Generalized Face Antispoofing by Learning to Fuse Features From High- and Low-Frequency Domains

In this article, we propose a face spoofing detection method by learning to fuse high-frequency (HF) and low-frequency (LF) features, in an effort to improve the generalization capability and fill up the domain gap between training and testing when the antispoofing is practically conducted in unseen scenarios. In particular, the proposed face antispoofing model consists of two streams that extract HF and LF components of a facial image with three high-pass and three low-pass filters. Moreover, considering the fact that spoofing features exist in different feature levels, we train our network with a novel multiscale triplet loss. The cross-frequency spatial attention module further enables the two streams to communicate and exchange information with each other. Finally, the outputs of the two streams are fused with a weighting strategy for final classification. Extensive experiments conducted on intra- and cross-database settings show the superiority of the proposed scheme.

From RGB to Depth: Domain Transfer Network for Face Anti-Spoofing

With the rapid development in face recognition, most of the existing systems can perform very well in unconstrained scenarios. However, it is still a very challenging task to detect face spoofing attacks, thus face anti-spoofing has become one of the most important research topics in the community. Though various anti-spoofing models have been proposed, the generalisation capability of these models usually degrades for unseen attacks in the presence of challenging appearance variations, e.g. , background, illumination, diverse spoofing materials and low image quality. To address this issue, we propose to use a Generative Adversarial Network (GAN) that transfers an input face image from the RGB domain to the depth domain. The generated depth clue enables biometric preservation against challenging appearance variations and diverse image qualities. To be more specific, the proposed method has two main stages. The first one is a GAN-based domain transfer module that converts an input image to its corresponding depth map. By design, a live face image should be transferred to a depth map whereas a spoofing face image should be transferred to a plain (black) image. The aim is to improve the discriminative capability of the proposed system. The second stage is a classification model that determines whether an input face image is live or spoofing. Benefit from the use of the GAN-based domain transfer module, the latent variables can effectively represent the depth information, complementarily enhancing the discrimination of the original RGB features. The experimental results obtained on several benchmarking datasets demonstrate the effectiveness of the proposed method, with superior performance over the state-of-the-art methods. The source code of the proposed method is publicly available at https://github.com/coderwangson/DFA .

Face Spoof Attack Detection with Hypergraph Capsule Convolutional Neural Networks

Face Spoof Attack Detection with Hypergraph Capsule Convolutional Neural Networks

SqueezeFace: Integrative Face Recognition Methods with LiDAR Sensors

In this paper, we propose a robust and reliable face recognition model that incorporates depth information such as data from point clouds and depth maps into RGB image data to avoid false facial verification caused by face spoofing attacks while increasing the model’s performance. The proposed model is driven by the spatially adaptive convolution (SAC) block of SqueezeSegv3; this is the attention block that enables the model to weight features according to their importance of spatial location. We also utilize large‐margin loss instead of softmax loss as a supervision signal for the proposed method, to enforce high discriminatory power. In the experiment, the proposed model, which incorporates depth information, had 99.88% accuracy and an F1 score of 93.45%, outperforming the baseline models, which used RGB data alone.

Segmentation Method for Face Modelling in Thermal Images

Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epochs and 131 iterations. The final model predicted and located the face correctly using the test image.

Fuzzy local ternary pattern and skin texture properties based countermeasure against face spoofing in biometric systems

AbstractEvaluation of facial skin texture properties is very demanding and noticeable due to its optimum resource constraint along with reduced processing expenditure. Traditionally, the local binary pattern (LBP) and the variations are accepted for analyzing the skin for liveness detection. LBP descriptor, which is the most dominant algorithm, has its own limitations of not working excellent on images with noise. Texture properties contain variations in skin features like wrinkles. LBP organizes the feature extraction for specified texture to extract the variation between normal and abnormal. In applications such as surveillance, the images are captured by remote cameras and transmitted to control room. The images shall be affected by transmission noises. Transmission noises are varied under the transmission of texture features quality, contrast based pixel coordinates. Thus, in the proposed work, the effectiveness of fuzzy local ternary pattern (FLTP) as a substitute LBP and LTP is presented. It is used to find uncertainty principles of texture image, which are trained from the local databases. Moreover, FLTP embraces Weber's law that fuses the features to wipe out the predetermined threshold setting in LTP. The proposed approach was analyzed on various publicly available databases pertaining to this domain. Furthermore the proposed system was tested with University Putra Malaysia (UPM) face spoof database. The outcomes obtained from the projected FLTP texture descriptor achieved most proper exactness and outflanked the LBP and LTP surface descriptors.

3D Face Anti-Spoofing With Factorized Bilinear Coding

We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. When compared with widely studied 2D face presentation attacks, 3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3D characteristics of materials similar to real faces. In this work, we tackle the problem of detecting these realistic 3D face presentation attacks and propose a novel anti-spoofing method from the perspective of fine-grained classification. Our method, based on factorized bilinear coding of multiple color channels (namely MC_FBC), targets at learning subtle fine-grained differences between real and fake images. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection. A large-scale wax figure face database (WFFD) with both images and videos has also been collected as super realistic attacks to facilitate the study of 3D face presentation attack detection. Extensive experimental results show that our proposed method achieves the state-of-the-art performance on both our own WFFD and other face spoofing databases under various intra-database and inter-database testing scenarios.

Face spoofing detection based on chromatic ED-LBP texture feature

Face spoofing detection, also known as liveness detection, is a challenging and one of the most active research areas in computer vision. In this paper, a novel texture descriptor, namely equilibrium difference local binary pattern (ED-LBP), is proposed for the representation and recognition of face texture. First, the adjacent pixels discrepancy in a facial image is fully considered and the discrepancy information is encoded into LBP. This novel texture feature extraction method has the advantage of getting more elaborate texture information without increasing the feature dimension. Second, the texture signatures in different color channels are fully investigated. More specifically, the feature histograms are calculated over each image band separately. Third, by the integration of the chromatic ED-LBP histograms and the two-level spatial pyramid, the local structure information of face is encoded in our approach which can well describe the differences between facial videos of valid users and impostors. Finally, the ED-LBP histograms from different color spaces are usually cascaded into a united feature vector which is feed into SVM for classification identification. Extensive experiments on four challenging and publicly available face anti-spoofing databases, namely CASIA FASD, Replay-Attack, Replay-Mobile, and OULU-NPU, demonstrate the effectiveness of our proposed approach. The results indicate that our methods are superior to state-of-the-art techniques and can effectively resist photo and video spoofing attacks in face recognition (FR).

Face spoofing detection via ensemble of classifiers toward low-power devices

Facial biometrics tend to be spontaneous, instinctive and less human intrusive. It is regularly employed in the authentication of authorized users and personnel to protect data from violation attacks. A face spoofing attack usually comprises the illegal attempt to access valuable undisclosed information as a trespasser attempts to impersonate an individual holding desirable authentication clearance. In search of such violations, many investigators have devoted their efforts to studying either visual liveness detection or patterns generated during media recapture as predominant indicators to block spoofing violations. This work contemplates low-power devices through the aggregation of Fourier transforms, different classification methods and handcrafted descriptors to estimate whether face samples correspond to falsification attacks. To the best of our knowledge, the proposed method consists of low computational cost and is one of the few methods associating features derived from both spatial and frequency image domains. We conduct experiments on recent and well-known datasets under same and cross-database settings with artificial neural networks, support vector machines and partial least squares ensembles. Results show that although our methodology is geared for resource-limited single-board computers, it can produce significant results, outperforming state-of-the-art approaches.

Client-specific anomaly detection for face presentation attack detection

One-class anomaly detection approaches are particularly appealing for use in face presentation attack detection (PAD), especially in an unseen attack scenario, where the system is exposed to novel types of attacks. This work builds upon an anomaly-based formulation of the problem and analyses the merits of deploying client-specific information for face spoofing detection. We propose training one-class client-specific classifiers (both generative and discriminative) using representations obtained from pre-trained deep Convolutional Neural Networks (CNN). In order to incorporate client-specific information, a distinct threshold is set for each client based on subject-specific score distributions, which is then used for decision making at the test time. Through extensive experiments using different one-class systems, it is shown that the use of client-specific information in a one-class anomaly detection formulation (both in model construction as well as decision boundary selection) improves the performance significantly. We also show that anomaly-based solutions have the capacity to perform as well or better than two-class approaches in the unseen attack scenarios. Moreover, it is shown that CNN features obtained from models trained for face recognition appear to discard discriminative traits for spoofing detection and are less capable for PAD compared to the CNNs trained for a generic object recognition task.

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.

A FACE SPOOFING DETECTION SYSTEM USING SURF ANALYSIS BASED ON GENETIC ALGORITHM AND ARTIFICIAL INTELLIGENCE TECHNIQUE.

Modern face biometric systems are susceptible to spoofing attacks and a secure face spoof detection system demands the capability to recognize whether a face is from a real person or a spoofed image that is created by an unauthenticated person. Inspired by the feature selection algorithm, characterization of printing artifacts, and differences in light reflection, we proposed to approach the problem of spoofing detection from a pattern analysis point of view. Indeed, face prints often contain printing quality faults that can be well detected using pattern features, the Speech up Robust Feature (SURF) descriptor. Hence, introduces a novel approach based on face pattern image analysis to find out if there is live in front of a camera or a printed face. The proposed approach analyzes the pattern and quality of the facial images using the SURF descriptor as a feature extraction algorithm. Compared to a lot of previous works, our proposed face spoofing detection approach is robust, computationally fast, and does not require user-cooperation. In addition, the feature optimization technique is used for the selection of a unique feature set from the ROI of face images. Convolutional Neural Network (CNN) classifier is used for the training of the proposed spoof detection system. It is seen that the designed hybrid system face spoof detection achieves high performance than the existing system and execution time is also well. The proposed method is assessed using the MATLAB simulator in computer vision and image processing toolbox. The experimental analysis on a publicly accessible database presented brilliant results compared to existing works by using the concept of feature optimization and artificial intelligence technique.

Face Spoofing Detection Using Improved SegNet Architecture with Blur Estimation Technique

Biometrics has been increasingly used as the well-known technology for the identification and verification of a person. Among the different biometric traits, the face has been extensively used for human identification and is therefore much vulnerable to face spoofing attacks. In this proposed work, the face is detected with the help of an improved SegNet-based architecture, with blur measure on the basis of local min-max of left and right edges and calculate blur of horizontal and vertical edges. Image filtering is done by an adaptive median filter (AMF). The proposed and novel five-layer encoder decoder SegNet-based algorithm improves the accuracy on various benchmark dataset, i.e., NUAA, replay, printed, CASIA and live database for face liveness detection. The detection rate has reached up to 97% and the time taken for liveness is reduced up to one sec per image. This proposed algorithm shows better value of recall, precision and error rate as compared to earlier algorithms.

Deteksi Spoofing Wajah Menggunakan Faster R-CNN dengan Arsitektur Resnet50 pada Video

Deteksi wajah merupakan proses mendasar dan penting dalam bidang pengenalan wajah yang sudah diteliti secara luas. Tujuan deteksi wajah adalah menentukan keberadaan dan menandai posisi wajah, baik pada gambar maupun video, yang disebut dengan bounding box. Salah satu masalah penting dalam deteksi wajah adalah membedakan wajah spoof dan non-spoof yang disebut sebagai deteksi spoofing wajah. Deteksi spoofing wajah merupakan pekerjaan penting yang digunakan untuk menjamin keamanan otentikasi berbasis wajah dan sistem analisis wajah. Oleh karena itu, dibutuhkan sebuah model yang dapat mendeteksi spoofing wajah. Pada makalah ini dilakukan proses membangun model yang dapat digunakan untuk mendeteksi wajah spoof dan non-spoof pada video menggunakan algoritme Faster R-CNN dengan arsitektur Resnet50. Faster R-CNN merupakan salah satu algoritme yang unggul dalam menyelesaikan berbagai persoalan deteksi objek. Dataset yang digunakan adalah Replay-Attack Database yang disediakan oleh Idiap Dataset Distribution Portal. Pada tahap training digunakan 360 video spoof dan non-spoof. Rata-rata nilai akurasi yang dihasilkan pada tahap training adalah 97,07%, dengan jumlah epoch sebanyak 21. Hasil pengujian menunjukkan bahwa model yang dihasilkan berhasil menentukan bounding box dengan akurat dan mendeteksi spoof dan non-spoof wajah pada video dengan efektif.

A Study on Presentation Attack Detection in Thermal Infrared.

Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

Face anti-spoofing with generated near-infrared images

Reflection differences between live faces and spoof faces under near-infrared spectrum make near-infrared image based methods obtain superior performance for face anti-spoofing. However, for conventional face recognition systems, near-infrared image based methods need additional near-infrared equipment to capture the input near-infrared images. In this paper, we propose a novel face anti-spoofing method which exploits the clues in both visible light (VIS) images and near-infrared (NIR) images without utilizing any near-infrared equipment during testing. Specifically, we first propose a novel multiple categories image translation generative adversarial network (MCT-GAN) which generates corresponding NIR images for VIS live and spoof face images. Then we utilize convolution neural network to learn fusing features from both VIS images and corresponding generated NIR images for the goal of live and spoof face classification. Qualitative and quantitative experiments demonstrate that our method obtains excellent results compared to the state-of-the-art methods.