Face Presentation Attack Detection Using Deep Background Subtraction

  Citation: Benlamoudi, A.; Bekhouche, S.E.; Korichi, M.; Bensid, K.; Ouahabi, A.; Hadid, A.; Taleb-Ahmed, A. Face Presentation Attack Detection Using Deep Background Subtraction. Sensors 2022,22, 3760. https://doi.org/ 10.3390/s22103760 Academic Editors: Michal Choras, Rafal Kozik and Marek Pawlicki Received: 3 April 2022 Accepted: 12 May 2022 Published: 15 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Article Face Presentation Attack Detection Using Deep Background Subtraction Azeddine Benlamoudi 1, Salah Eddine Bekhouche 2, Maarouf Korichi 1, Khaled Bensid 1, Abdeldjalil Ouahabi 3,* , Abdenour Hadid 4and Abdelmalik Taleb-Ahmed 4 1 Laboratoire de Génie Électrique, Faculté des Nouvelles Technologies de l’Information et de la Communication, Université Kasdi Merbah Ouargla, Ouargla 30 000, Algeria; [email protected] (A.B.); [email protected] (M.K.); [email protected] (K.B.) 2 Department of Computer Science and Artificial Intelligence, Faculty of Informatics, University of the Basque Country UPV/EHU, 20018 San Sebastian, Spain; [email protected] 3UMR 1253, iBrain, INSERM, Université de Tours, 37000 Tours, France 4Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, Université de Lille, CNRS, 59313 Valenciennes, France; abdenour[email protected] (A.H.); [email protected] (A.T.-A.) *Correspondence: [email protected] Abstract: Currently, face recognition technology is the most widely used method for verifying an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person’s face is used to obtain access to services. Based on a combination of background subtraction (BS) and convolutional neural network(s) (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face presentation attack detection algorithm. This algorithm includes a fully connected (FC) classifier with a majority vote (MV) algorithm, which uses different face presentation attack instruments (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the face anti-spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results are very interesting and are much better than those obtained by state-of-the-art methods. For instance, on the REPLAY-ATTACK database, we were able to attain a half-total error rate (HTER) of 0.62% and an equal error rate (EER) of 0.58%. We attained an EER of 0% on both the CASIA-FASD and the MSU MFSD databases. Keywords: biometrics; face presentation attack; deep learning 1. Introduction Individuals can be successfully identified and authenticated using biometric features and traits. Hence, it is appropriate for access control and global security systems that depend on person recognition, which is achieved through the use of a variety of biometric modalities, ranging from the classic fingerprint through the face, iris, ear [ 1 – 4 ] and, more recently, vein and blood flow. Furthermore, a number of spoofing methods have been developed in order to overcome such biometric systems [ 5 ]. When someone tries to get around a face biometric system by placing a fake face in front of the camera, this is known as a presentation attack. Nevertheless, compared to other modalities, the abundance of still face images or video sequences on the internet has made it exceptionally easy to obtain a person’s facial data. The spoofing detection literature discusses multiple types of presentation attack instruments, such as print, replay, silicon masks, and makeup attacks. The focus of our work is on the first two attacks, namely print and replay attacks. The print attack spoofs 2D face recognition systems by using printed photographs of a subject, whereas the replay Sensors 2022,22, 3760. https://doi.org/10.3390/s22103760 https://www.mdpi.com/journal/sensors Sensors 2022,22, 3760 2 of 17 attack presents a video of a bona fide presentation to avoid liveness detection. Furthermore, the low cost of launching a face presentation attack instrument has increased the prevalence of the problem. Face recognition system spoofing media ranges from low-quality paper prints to high-quality photographs, as well as video streams played in front of the biometric authentication system sensor. Feature extraction is a critical component of the face presentation attack detection task when using a classical machine learning classifier. Convolutional neural network(s) (CNN) can also be used to predict scores. This latter is a crucial component of deep learning algorithms, such as the ResNet-50 [ 6 ] pre-trained model, which has been studied for a few years under a variety of conditions and scenarios. In our work, we used background subtraction (BS) with CNN to predict each frame in the input video and rank the score using the MV algorithm to determine whether the input video is real or fake. Inspired by the work of frame difference and multilevel representation (FDML) [7], we propose an effective system for face presentation attack detection. To do this, we suggest using the background substruction method in the preprocessing step to adjust the face’s motion. The MV algorithm is used to improve the performance rate as well as the decision of the input video after predicting the score of each frame by ResNet-50. To test our system, we used videos from numerous public face spoof databases with varying quality, resolutions, and dynamic ranges. We also compared our results to those of a number of current state-of-the-art approaches. The following are the main contributions of this work: • Improving face presentation attack detection using BS that discriminates the motion of real face from a fake one. • Fine-tuning the ResNet-50 model for the face presentation attack detection task to extract meaningful deep facial features. • Using the MV algorithm to increase the classification rate of the system, which is clearly observed when the methodology outperformed previous methodologies in the literature, according to the results of our experiments. • Tackling the sensor interoperability problem by including the experiments of interdatabase and intra-database tests. The remainder of the paper is structured as follows. Section 2describes related work on face presentation attack detection. Then, our approach is described in detail in Section 3. Section 4summarizes the experimental results and provides a comparative analysis. The section also describes the databases that we used in our tests. Section 5draws some conclusions and highlights some future directions. Abbreviations define the main acronyms used in this paper. 2. Related Work Presentation attack can be detected in a variety of ways. In this paper, we focus on two types of face presentation attack detection methods: handcrafted and deep learningbased methods. In this section, we present most previous work in face presentation attack detection. However, we only focus on those that are thematically closer to our goals and contributions. 2.1. Handcraft-Based Techniques Texture features, which can describe the contents and details of a specific region in an image, are an important low-level feature in face presentation attack detection methods. Therefore, the analysis of image texture information is used in many techniques, such as compressed sensing, which preserves texture information and denoising at the same time [ 8 , 9 ]. These techniques based on handcrafted features provide accurate features that increase the detection rate of a spoofing system. Smith et al. [ 10 ] proposed a method for countering attacks on face recognition systems by using the color reflected from the user’s face as displayed on mobile devices. The presence or absence of these reflections can be utilized to establish whether or not the images were captured in real time. The algorithms use simple RGB images to detect presentation attack. These strategies can be classified into Sensors 2022,22, 3760 3 of 17 two categories: static and dynamic approaches. The static is used on a single image, whilst dynamic is used on the video. The majority of approaches for distinguishing between real and synthetic faces are focused on texture analysis. Arashloo et al. [ 11 ] combined two spatial–temporal descriptors using kernel discriminant analysis fusion. They are multiscale binarized statistical image features on three orthogonal planes (MBSIF-TOP) and multiscale local phase quantization on three orthogonal planes (MLPQ-TOP). To distinguish between real and fake individuals, Pereira et al. [ 12 ] also experimented with a dynamic texture that was based on local binary pattern on three orthogonal planes (LBP-TOP). The good results of LBP-TOP are due to the fact that temporal information is crucial in face presentation attack detection. Tirunagari et al. [ 13 ] used local binary pattern(s) (LBP) for dynamic patterns and dynamic mode decomposition (DMD) for visual dynamics. Wen et al. [ 14 ] proposed an image distortion analysis-based method (IDA). To represent the face images, four different features were used: blurriness, color diversity, specular reflection, and chromatic moments, also relying on the features that can detect differences between real image and fake one without capturing any information about the user’s identity. Patel et al. [ 15 ] investigated the impact of different RGB color channels (R, G, B, and gray scale) and different facial regions on the performance of LBP and dense scale invariant feature transform (DSIFT) based algorithms. Their investigations revealed that extracting the texture from the red channel produces the best results. Boulkenafet et al. [ 16 ] proposed a color texture analysis-based face presentation attack detection approach. They employed the LBP descriptor to extract texture features from each channel after encoding the RGB images in two color spaces: HSV and YCbCr, and then concatenated these features to distinguish between real and fake faces. Some methods, such as [ 17 ], have recently used user-specific information to improve the performance of texture-based FAS techniques. Garcia et al. [ 18 ] proposed face presentation attack detection by looking for Moiré patterns caused by digital grid overlap where their detection is based on frequency domain peak detection. For classification, they used support vector machines (SVM) with an radial basis function kernel. They started to run their tests on the Replay Attack Corpus and Moiré databases. Other face presentation attack detection solutions are based on textures on 3D models, such as those used in [ 19 ]. Because the attacker in 3D models utilizes a mask to spoof the system, the introduction of wrinkles might be extremely helpful in detecting the attack. The presented work in [ 19 ] examines the viability of performing low-cost assaults on 2.5D and 3D face recognition systems using self-manufactured three-dimensional (3D) printed models. 2.2. Deep Learning-Based Techniques Actually, deep learning is used in a variety of systems and applications for biometric authentication [ 20 ], where the deep network can be trained using a number of patterns. After learning all of the dataset’s unique features, the network can be used to identify similar patterns. Deep learning approaches have mostly been used to learn face presentation attack detection features. Moreover, deep learning is efficient at classification (supervised learning) and clustering tasks (unsupervised learning). Thus, the system assigns class labels to the input instances in a classification task, but the instances in clustering approaches are clustered based on their similarity without the usage of class labels. To train models with significant discriminative abilities, Yang et al. [ 21 ] used a deep CNN rather than manually constructing features from scratch. Quan et al. proposed a semisupervised learning-based architecture to fight face presentation attack threats using only a few tagged data, rather than depending on time-consuming data annotations. They assess the reliability of selected data pseudo labels using a temporal consistency requirement. As a result, network training is substantially facilitated. Moreover, by progressively increasing the contribution of unlabeled target domain data to the training data, an adaptive transfer mechanism can be implemented to eliminate domain bias. According to the authors in [ 22 ], they use a type of ground through (GT) termed appr-GT in conjunction with the identity information of the spoof image to generate a genuine image of the appropriate subject in Sensors 2022,22, 3760 4 of 17 the training set. A metric learning module constrains the generated genuine images from the spoof images to be near the appr-GT and far from the input images. This reduces the effect of changes in the imaging environment on the appr-GT and GT of a spoof image. Jia et al. [ 23 ] proposed a unified unsupervised and semi-supervised domain adaptation network (USDAN) for cross-scenario face presentation attack detection, with the purpose of reducing the distribution mismatch between the source and target domains. The marginal distribution alignment module (MDA) and the conditional distribution alignment module (CDA) are two modules that use adversarial learning to find a domain-invariant feature space and condense features of the same class. Raw optical flow data from the clipped face region and the complete scene were used to train a neural network by Feng’s team et al. [ 24 ]. Motion-based presentation attack detection does not need a scenic model or motion assumption to generalize. They present an image quality-based and motion-based liveness framework that can be fused together using a hierarchical neural network. In their work [ 25 ], Liu et al. proposed a deep tree network (DTN) that learns characteristics in a hierarchical form and may detect unanticipated presentation attack instrument by identifying the features that are learned. Yu et al. [ 26 ] introduces two new convolution and pooling operators for encoding finegrained invariant information: central difference convolution (CDC) and central difference pooling (CDP). CDC outperforms vanilla convolution in extracting intrinsic spoofing patterns in a number of situations. As described in Qin et al. [ 27 ], adaptive inner-update (AIU) is a novel meta learning approach that uses a meta-learner to train on zeroand few-shot FAS tasks utilizing a newly constructed Adaptive Inner update Meta Face Anti spoofing (AIM-FAS). According to Yu et al. [ 28 ], the multi-level feature refinement module (MFRM) and material-based multi-head supervision can help increase BCN’s performance. In the first approach, local neighborhood weights are reassembled to create multi-scale features, while in the second, the network is forced to acquire strong shared features in order to perform tasks with multiple heads. CDC-based frame level FAS approaches, proposed by the authors in [ 29 ], have been developed. These patterns can be captured by aggregating information about intensity and gradient. In comparison to a vanilla convolutional network, the central difference convolutional network (CDCN) built with CDC has a more robust modeling capability. CDCN++ is an improved version of CDCN that incorporates the search backbone network with the multiscale attention fusion module (MAFM) for collecting multi-level CDC features effectively. Spatiotemporal anti-spoof network (STASN) is a new attention mechanism invented by Yang et al. [ 30 ] that combines global temporal and local spatial information, allowing them to examine the model’s understandable behaviors. To improve CNN generalization, Liu et al. [ 31 ] proposed to use innovative auxiliary information to supervise CNN training. A new CNN-RNN architecture for learning the depth map and rPPG signal from end-to-end is also proposed. Wang et al. [32] proposed a depth-supervised architecture that can efficiently encode spatiotemporal information for presentation attack detection and develops a new approach for estimating depth information from several RGB frames. Short-term extraction is accomplished through the use of two unique modules: the optical flow-guided feature block (OFFB) and the convolution gated recurrent units (ConvGRU). Jourabloo et al. [ 33 ] proposed a new CNN architecture for face presentation attack detection, with appropriate constraints and supplementary supervisions, to discern between living and fake faces, as well as long-term motion. In order to detect presentation attacks effectively and efficiently, Kim et al. [ 34 ] introduced the bipartite auxiliary supervision network (BASN), an architecture that learns to extract and aggregate auxiliary information. Huszár et al. [35] proposed a deep learning (DL) approach to address the problem of presentation attack instruments occurring from video. The approach was tested in a new Sensors 2022,22, 3760 5 of 17 database made up of several videos of users juggling a football. Their algorithm is capable of running in parallel with the human activity recognition (HAR) in real-time. Roy et al. [36] proposed an approach called the bi-directional feature pyramid network (BiFPN) to detect presentation attacks because the approach containing high-level information demonstrates negligible improvements. Ali et al. [ 37 ]—based on stimulating eye movements by using the use of visual stimuli with randomized trajectories to detect presentation attack instrument. Ali et al. [ 38 ]—by the combination of two methods, which are head-detection algorithm and deep neural network-based classifiers. The test involved various face presentation attacks in thermal infrared in various conditions. It appears that most of the existing handcraft and deep learning-based features may not be optimal for the FAS task due to the limited representation capacity for intrinsic spoofing features. In order to learn more robust features for the domain shift as well as more discriminative patterns for liveness detection, we propose deep background subtraction and majority vote algorithm to take into account both dynamic and static information. 3. Proposed Approach Figure 1describes the overall structure of our proposed approach, which is divided into three modules: background subtraction, feature learning, and data classification. To begin, we used the background subtraction between consecutive frames to extract motion, we can also call this technique BS. Then, the features were extracted using the ResNet-50 transfer learning model on the foreground of BS. Finally, to distinguish between real and fake faces of each frame, we used a classification layer that employed a fully connected layer. After that, we used MV to predict whether the input video was real or not. In the subsections that follow, all subsystems (modules) will be discussed. Input stream Output masks Classification Module Features learning using transfer learning Resnet50 Dropout + Relu Classification Layer (With metrics updates) Layer 1 x 2 Real Fake Majority Votes Real Fake FD FD FD FD FD ResNet50 Output FC Layer 1x1024 Real Fake Real Fake Real Fake Real Fake Background Subtraction Module Feature Learning Module Figure 1. Framework of our proposed approach. 3.1. Background Subtraction Module In our research work, we used a face presentation attack detection system based on an extended BS algorithm. The background subtraction approach, which is based on the premise of obtaining the pixels in the image sequence difference operation to do two or three continuous frames, is the most commonly used action target detection measure. Using an image pixel value obtained by subtracting the difference image and the binarized difference image, if the pixel value change threshold is less than a predefined one, we can feel this as a background pixel in the adjacent frame. If the pixel value of an image area changes dramatically, it is possible to deduce that this is due to the action of detecting Sensors 2022,22, 3760 6 of 17 spoof in the image caused by these symbols as foreground pixel regions. While taking dynamic information into account, a pixel region based on symbolic actions can determine the position of the target in the image. Background subtraction is applied to images and the threshold result is displayed as a foreground image. Figure 2shows an example of the output. This is a low-cost and ineffective method of detecting motion in a video stream. The image Pt is transformed into a grey-scale (intensity) image It . Then, given the image It and the previous image It−1 , the current output is Rt, where: Rt(x,y) = It(x,y)if |It(x,y)−It−1(x,y)|>T 0 otherwise (1) T is the value of the threshold parameter. In our situation, we just utilized a threshold to remove the pixels with the same values across the two frames. The foreground pixels take the value of the current frame if there is motion. The foreground pixel is set to zero if there is no motion. We apply a moving window mechanism that takes the first frame within a window of 5 frames. This means that we take a frame and drop 4 frames in each cycle. For example, in a video of 150 frames, we use 30 frames and drops 120 frames. Real Printed Replay RGB Gray BS Figure 2. Example of a genuine face and corresponding print and replay attacks in grey-scale and BS. 3.2. Feature Learning Module Feature learning (FL) is a set of approaches in machine learning that allow a system to discover the representation needed for feature detection, prediction, or classification from a preprocessed dataset automatically. This enables a machine to learn the features and apply them to a specific task-like classification and prediction. Feature learning can be achieved in deep learning by either creating a complete CNN to train and test the collection of images or adapting a pre-trained CNN for classification or prediction for the new images-set. Transfer learning is the latter strategy used in the deep learning domain. Transfer learning is a machine learning technique in which a model created for one task is utilized as the basis for a model on a different task. Transfer learning (TL) is commonly used in deep learning (DL) applications to allow you to use a pre-trained network for solving new classification tasks. To meet the new learning tasks, the learning parameters of the pre-trained network with randomly initialized Sensors 2022,22, 3760 7 of 17 weights must be fine-tuned. Transfer learning is typically considerably faster and easier to learn/train than building a network from the initial concept. Transfer learning is an optimization and a quick way that can save time or improve efficiency. In this section, a transfer learning technique is applied by fine-tuning a pretrained ResNet-50 model on ImageNet dataset using multiple spoofing datasets where the output of the last FC layer is changed to output two classes (real/fake). The network is called ResNet-50, due to the fact that it has 48 convolution layers, along with 1 MaxPool and 1 Average Pool layer, and it introduced the use of residual blocks. 3.3. Classification Module Data classification is a vital process for separating large datasets into classes for decision-making, pattern detection, and other purposes. For multi-class classification problems with mutually exclusive classes, a classification layer uses a fully connected layer to compute the cross-entropy loss. The features from ResNet-50 are passed via a FC layer made of 1024 neurons with a 40% dropout to prevent over-fitting in the classification module. Having followed that, the units were activated with a rectification mechanism called ReLU. MAX (X, 0) is the ReLu function, which sets all negative values in the matrix X to zero while keeping all other values constant. The reason for choosing ReLU is that deep network training with ReLU tended to converge considerably faster and more reliably than deep network training with sigmoid activation. Finally, the output layer comprised of one neuron unit programmed to compute probabilities for the classes using the sigmoid function (binary classifier). A sigmoid is a mathematical function that takes a vector of k real values and changes it to a two-probability probability distribution. We employed voting ensemble in our tests to classify each subject (video) as real or fake. A voting ensemble (sometimes known as a "majority voting ensemble") is a machine learning model that incorporates predictions from several other models, such as multiple predictions in each frame after the input video’s last layer (classification layer). The predictions for each label are combined, and the label with the majority vote is forecasted (see Figure 1, classification module) to determine if the input video belongs to a real or fake one. The majority voting ensemble creates forecasts based on the most common one. It is a strategy that can be utilized to boost performance, with the goal of outperforming every frame used independently in the ensemble. 4. Experimental Results and Analysis In this section, the employed benchmark datasets will be introduced first, followed by a brief description of the evaluation criteria. After that, we present and analyze a series of experiments that we assume demonstrate the efficacy of the proposed BS-CNN+MV based face presentation attack detection technique. 4.1. Database and Protocol In order to assess of the effectiveness of our proposed presentation attack detection technique, we performed a set of experiments on well known databases where the top three challenging databases were used: the CASIA-FASD (http://www.cbsr.ia.ac. cn/english/FaceAntiSpoofDatabases.asp (accessed on 4 July 2014)); face anti-spoofing database, Replay-Attack (https://www.idiap.ch/dataset/replayattack (accessed on 6 August 2014)) database, and MSU (https://drive.google.com/drive/folders/1nJCPdJ7R6 7xOiklF1omkfz4yHeJwhQsz (accessed on 8 May 2014)) mobile face spoofing databases. Those databases contain video recordings of real and fake attacks. A brief description of these databases is given as fellow: The CASIA-FASD database [ 39 ] is a dataset for face presentation attack detection. This database contains 50 genuine subjects in total and the corresponding fake faces are captured with high quality from the original ones. Therefore each subject contains 12 videos (3 genuine and 9 fake) under three different resolutions and light conditions, namely low Sensors 2022,22, 3760 8 of 17 quality, normal quality, and high quality. Moreover, three fake face attacks are designed, which include warped photo attack, cut photo attack, and video attack. The overall database contains 600 video clips and the subjects are divided into subsets for performing training and tests in which 240 videos of 20 subjects are used for training and 360 videos of 30 subjects for testing. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects (see Figure 3). Low Quality Normal Quality High Quality Real Face Warped Photo Attack Cut Photo Attack Video Attack Figure 3. Samples from the CASIA FASD database. Among the popular databases designed for the presentation attack detection application, one can find the Replay-Attack database [ 40 ]. This database consists of 1300 video of real-access and attack attempts to 50 subjects, (See Figure 4). However, these videos were taken using a built-in webcam on a MacBook laptop under two separate scenarios (controlled and adverse). In addition, two cameras were used to create the faked facial attack for each person in high-resolution images and videos: a Canon PowerShot SX150 IS and an iPhone 3GS camera. Moreover, fixed attacks and hand attacks are the two types of attacks. There are ten videos in each subset: four mobile attacks with a resolution of 480 × 320 pixels on an iPhone 3GS screen, then, by using a first generation iPad with a screen resolution of 1024 × 768 pixels, four high-resolution screen attacks were performed. On A4 paper, two hard-copy print attacks (printed on a Triumph-Adler DCC 2520 colour laser printer) occupied the whole available printing surface. It should be noted that the complete set of videos is divided into three non-overlapping subsets for training, development, and testing in order to evaluate them. The pattern recognition and image processing (PRIP) group at Michigan State University developed a publicly available MSU-MFSD [ 14 ] database for face presentation attacks. The database contains 280 video clips of attempted photo and video attacks on 35 clients. It was created using a mobile phone to capture both genuine and presentation attack. This was accomplished using two types of cameras: (1) the built-in camera in the MacBook Air 13 inch (640 × 480) and (2) the front-facing camera on the Google Nexus 5 Android phone (720 × 480). Each subject received two video recordings, the first of which was taken using a laptop camera and the second with an Android camera (See Figure 5). High-resolution video was recorded for each subject utilizing two devices to create the attacks: (1) Canon PowerShot 550D SLR camera, which captures 18.0 megapixel photos and 1080p highdefinition video clips; (2) iPhone 5S back-facing camera, which captures 1080p video clips. There are three types of presentation attack instruments, the first one (1) high-resolution Sensors 2022,22, 3760 9 of 17 replay video, the first type of presentation attack instrument is a high-resolution replay video attack using an iPad Air screen, with a resolution of 2048 × 1536, the second is a mobile phone replay video attack using an iPhone 5S screen, with a resolution of 1136 × 640, and the third is a printed photo attack using an A3 paper with a fully-occupied printed photo of the client’s biometry, with a paper size of: 11 × 17 (279 mm × 432 mm), printed with an HP LaserJet CP6015xh printer at a resolution of 1200 × 600 dpi. Finally, to assess performance, the 35 subjects in the MSU-MFSD database were divided into two subsets: 15 for training and 20 for testing. Adverse Scenario Controlled Scenario Real Access Photo Attack Fixed Photo Attack Hand Video Attack Hand Video Attack Fixed Figure 4. Examples of real accesses and attacks in different scenarios. 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