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.

scholarly journals Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 3309-3314
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Attack Detection ◽  
Detection Methods ◽  
Deep Convolutional Neural Networks ◽  
Replay Attack ◽  
Multiple Features ◽  
Spoofing Detection ◽  
Occurrence Matrix ◽  
Face Spoofing

Nowadays, face biometric-based access control systems are becoming ubiquitous in daily life while they are still vulnerable to spoofing attacks. Developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning-based detection methods cannot be updated to optimum due to limited data. In this paper,a highly accurate face spoof detection system using multiple features and deep learning is proposed. The input video is broken into frames using content-based frame extraction. From each frame, the face of the person is cropped.From the cropped images multiple features like Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN). Training and testing are performed separately by using collected sample data.Experiments on the standard spoof database called Replay-Attack database the proposed system outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Sajad Einy ◽  
Cemil Oz ◽  
Yahya Dorostkar Navaei
Keyword(s):  
Deep Learning ◽  
Computer Security ◽  
Feature Learning ◽  
Face Image ◽  
Public Access ◽  
Training Data ◽  
Learning Approach ◽  
Replay Attack ◽  
Spoofing Detection ◽  
Face Spoofing

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.

scholarly journals An Anomaly Detection Approach to Face Spoofing Detection: A New Formulation and Evaluation Protocol

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 13868-13882 ◽  
Author(s):  
Shervin Rahimzadeh Arashloo ◽  
Josef Kittler ◽  
William Christmas
Keyword(s):  
Anomaly Detection ◽  
Evaluation Protocol ◽  
Detection Approach ◽  
Spoofing Detection ◽  
New Formulation ◽  
Face Spoofing

scholarly journals Liveness Detection with Opencv

2019 ◽  
Vol 8 (9S3) ◽  
pp. 583-585
Keyword(s):  
Social Media ◽  
Face Recognition ◽  
Smart Phone ◽  
Recognition System ◽  
Biometric System ◽  
Biometric Recognition ◽  
Authorized User ◽  
Face Recognition System ◽  
Behavioural Characteristics ◽  
Face Spoofing

Biometric system is widely used to recognize the authorized person based on either behavioural characteristics or physical. But this can be spoofed using various traits. Spoofing attack is nothing but attacking or harming biometric recognition system using security features to use system without permission of authorized user. Images or video of person can be easily available from social media or can be easily captured from some distance. Consider what would happen if a nefarious user tried to purposely circumvent your face recognition system. Such a user could try to hold up a photo of another person. Maybe they even have a photo or video on their smart phone that they could hold up to the camera responsible for performing face recognition .In the project, we have tried spotting these “fake” versus “real/legitimate” faces and how we could apply anti-face spoofing algorithms into our facial recognition applications by applying live ness detection[1] with Open CV.

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