Face spoofing detection using surface and sub-surface reflections analysis

Reflection based analysis has been used in previous research for various objectives. Materials classification is one of them. Basically, each material consists of two types of reflections: surface and sub-surface. To separate these two reflections, polarized light could be applied. Previously, multi-reflections characteristics were analyzed using polarized light to classify objects such as between metals and non-metals. However, no trial has been done using the same method to distinguish real and fake faces that could be used to combat spoofing attempts in face biometric system. Since human skin is multi layers structure, it also produces multi reflections. In this paper, driven by the theory, surface and sub-surface reflections of both genuine human face and paper face mask were statistically examined. In addition, iPad displayed face images were also used as spoofing attempts. Images of genuine and spoofing faces were captured using polarized light under two different polarization angles: 0 and 90 degrees. Each angle captured images with surface and sub-surface reflections, accordingly. Those reflections were analyzed based on the mean, standard deviation, skewness and kurtosis. Modality distribution of each image was also studied using another method called the bimodality coefficient (BC). From the results, it is not possible to distinguish between genuine face and printed photos because of the multi reflections’ similarities. However, iPad displayed face images have been successfully identified as spoofing trials.

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 F 1 score of 93.45%, outperforming the baseline models, which used RGB data alone.

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.

Face Recognition Using Livenessnet

Every person's face is unique, although have the same structure such as noise, eyes, lips, etc. but it can vary strikingly. It’s within this variance which lies in the distinguishing characteristics that can be used to identify one person from another. Face recognition is a popular concept which is commonly used in surveillance cameras at public places for security purposes. With the advancement of digital technologies, the demand for security to provide access control is increasing. It uses various methods of authentication to keep all details secure, such as a system focused on encrypted user name & password, smart card, biometrics, etc. The “Face Recognition using DNN with LivenessNet” presents a face recognition method based on deep neural networks for liveness. Any algorithm is considered to be efficient only if it is robust and accurate. It provides accurate results with face spoofing quickly and efficiently. The main advantage of using this technique is identifying the uniqueness in the datasets by capturing the real-time face data through different modes & jitter. It provides accurate face recognition model which can be used for safety and security purpose.

A Review on Face Anti-Spoofing

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

New Features for Webcam Proctoring Using Python and Opencv

Online examinations have turned out to be the new normal. However, it is not that easy to proctor the students as rigorously as in in-center examinations. It is essential to find an approach to proctor the online examinations too as rigorously as possible. There are already several webcam proctoring systems that are used in the real world, but these systems are not very accurate and miss out on detecting all possible malpractices and in certain cases due to defect in the system it detects a malpractice for someone who never even attempted any. This project focuses mainly on building features that can make the existing webcam proctoring system more advanced and rigorous. The project is aimed at building the following features namely head pose estimation, mouth opening detection, eye ball monitoring, number of persons detection, mobile phone detection and face spoofing detection. For each of these features, machine learning models are built using Python. All these features make use of the live webcam feed which is obtained using OpenCV and an output is obtained which gives information about the direction of the head and eyes, presence of more than one person and presence of mobile phone, opening of mouth, occurrence of face spoofing. All these outputs are recorded as a log file which can be used to identify any possible malpractices based on these features.

Face Antispoofing Method Using Color Texture Segmentation on FPGA

User authentication for accurate biometric systems is becoming necessary in modern real-world applications. Authentication systems based on biometric identifiers such as faces and fingerprints are being applied in a variety of fields in preference over existing password input methods. Face imaging is the most widely used biometric identifier because the registration and authentication process is noncontact and concise. However, it is comparatively easy to acquire face images using SNS, etc., and there is a problem of forgery via photos and videos. To solve this problem, much research on face spoofing detection has been conducted. In this paper, we propose a method for face spoofing detection based on convolution neural networks using the color and texture information of face images. The color-texture information combined with luminance and color difference channels is analyzed using a local binary pattern descriptor. Color-texture information is analyzed using the Cb, S, and V bands in the color spaces. The CASIA-FASD dataset was used to verify the proposed scheme. The proposed scheme showed better performance than state-of-the-art methods developed in previous studies. Considering the AI FPGA board, the performance of existing methods was evaluated and compared with the method proposed herein. Based on these results, it was confirmed that the proposed method can be effectively implemented in edge environments.