scholarly journals Face Antispoofing Method Using Color Texture Segmentation on FPGA

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Youngjun Moon ◽  
Intae Ryoo ◽  
Seokhoon Kim
Keyword(s):  
Local Binary Pattern ◽  
User Authentication ◽  
Color Difference ◽  
Face Images ◽  
Texture Information ◽  
Biometric Systems ◽  
Spoofing Detection ◽  
Real World Applications ◽  
Face Spoofing ◽  
Color Texture

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.

scholarly journals Detection of Face Spoofing using Color Texture and Edge Features

2020 ◽  
Vol 8 (5S) ◽  
pp. 33-37
Keyword(s):  
Recognition System ◽  
Replay Attack ◽  
Authorized User ◽  
Detection Techniques ◽  
Detection Approach ◽  
Spoofing Detection ◽  
Wide Scale ◽  
Edge Features ◽  
Face Spoofing ◽  
Color Texture

The wide scale use of facial recognition systems has caused concerns about spoofing attacks. Security is essential requirement for a face recognition system to provide reliable protection against spoofing attacks. Spoofing happens in situations where someone tries to behave as an authorized user to obtain illicitly access the protected system to gain advantage over it. In order to identify spoofing attacks, face spoofing detection approaches have been used. Traditional face spoofing detection techniques are not good enough as most of them focus only on the gray scale information and discarding the color information. Here a face spoofing detection approach with color texture and edge analysis is presented. The approach for investigating the texture of input images, Local binary pattern and Edge Histogram descriptor are proposed. Experiments on a publicly available dataset, Replay attack, showed excellent results compared to existing works.

Face spoofing detection with local binary pattern network

2018 ◽  
Vol 54 ◽  
pp. 182-192 ◽  
Author(s):  
Lei Li ◽  
Xiaoyi Feng ◽  
Zhaoqiang Xia ◽  
Xiaoyue Jiang ◽  
Abdenour Hadid
Keyword(s):  
Local Binary Pattern ◽  
Spoofing Detection ◽  
Face Spoofing

scholarly journals Spoof Detection Using Local Binary Pattern In Face

2020 ◽  
Vol 13 (1) ◽  
pp. 39
Author(s):  
Vani Dave
Keyword(s):  
Local Binary Pattern ◽  
Total Error ◽  
Problem Area ◽  
Authorized User ◽  
Facial Identity ◽  
Total Error Rate ◽  
Learning Techniques ◽  
Biometric Systems ◽  
Access Right ◽  
Face Spoofing

Spoofing attack is an attempt to acquire some other’s identity or access right by using a biometric evidence of authorized user. Among all biometric systems facial identity is one of the widely used method that is prone to such spoofing attacks using a simple photograph of the user. The paper focuses and takes the problem area of face spoofing attacks into account by detecting spoof faces and real faces. We are using the local binary pattern (LBP) for providing the solution of spoofing problem and with the help of these patterns we inspect primarily two types of attacks i.e. printed photograph and photos displayed using digital screen. For this, we will use the local database maintained by us having the images labeled as real and spoof for the data required. We conclude that local binary pattern will reduce the total error rate and will show the moderate output when used across a wide set of attack types. This will enhance the efficiency of the system for detection of spoofing by using the deep learning techniques

scholarly journals Face spoofing detection using surface and sub-surface reflections analysis

2021 ◽  
Vol 24 (1) ◽  
pp. 189
Author(s):  
Azim Zaliha Abd Aziz ◽  
Mohd Rizon Mohamed Juhari
Keyword(s):  
Polarized Light ◽  
Face Mask ◽  
Human Face ◽  
Biometric System ◽  
Face Images ◽  
Spoofing Detection ◽  
The Mean ◽  
Face Spoofing ◽  
Skewness And Kurtosis ◽  
Face Biometric

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.

scholarly journals Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

2018 ◽  
Vol 4 (10) ◽  
pp. 112 ◽  
Author(s):  
Mariam Kalakech ◽  
Alice Porebski ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Classification Problems ◽  
Class Label ◽  
Laplacian Score ◽  
Pattern Histogram ◽  
Color Texture

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

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