Liveness control in face recognition with deep learning methods (original) (raw)

Deep Learning meets Liveness Detection: Recent Advancements and Challenges

Cornell University - arXiv, 2021

Facial biometrics has been recently received tremendous attention as a convenient replacement for traditional authentication systems. Consequently, detecting malicious attempts has found great significance, leading to extensive studies in face anti-spoofing (FAS),i.e., face presentation attack detection. Deep feature learning and techniques, as opposed to hand-crafted features, have promised dramatic increase in the FAS systems' accuracy, tackling the key challenges of materializing realworld application of such systems. Hence, a new research area dealing with development of more generalized as well as accurate models is increasingly attracting the attention of the research community and industry. In this paper, we present a comprehensive survey on the literature related to deep-feature-based FAS methods since 2017. To shed light on this topic, a semantic taxonomy based on various features and learning methodologies is represented. Further, we cover predominant public datasets for FAS in a chronological order, their evolutional progress, and the evaluation criteria (both intra-dataset and inter-dataset). Finally, we discuss the open research challenges and future directions.

Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security

International Journal of Emerging Technology and Advanced Engineering

Face liveness detection is an important biometric authentication method for face recognition securitythat is used to determine a fake face from an authentic one. In this paper, a liveness detection method based on optimized LeNet-5 is proposed. The LeNet-5 is optimized by increasing the convolution kerneland byintroducing a global average pooling. The simulation results show that the proposed model obtained the highest recognition rate of 99.95% as against the 96.67% and 98.23% accuracy from the Support Vector Machine (SVM) and LeNet-5 models, respectively.The results denote that the proposed model has a high recognition rate in face liveness detection.

Face Liveness Detection – A Comprehensive Survey Based on Dynamic and Static Techniques

Abstract - With the wide acceptance of online systems, the desire for accurate biometric authentication based on face recognition has increased. One of the fundamental limitations of existing systems is their vulnerability to false verification via a picture or video of the person. Thus, face liveness detection before face authentication can be performed is of vital importance. Many new algorithms and techniques for liveness detection are being developed. This paper presents a comprehensive survey of the most recent approaches and their comparison to each other. Even though some systems use hardware-based liveness detection, we focus on the software-based approaches, in particular, the important algorithms that allow for an accurate liveness detection in real-time. This paper also serves as a tutorial on some of the important, recent algorithms in this field. Although a recent paper achieved an accuracy of over 98% on the liveness NUAA benchmark, we believe that this can be further improved through incorporation of deep learning. Index Terms — Face Recognition, Liveness Detection, Biometric Authentication System, Face Anti-Spoofing Attack. International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 10, October 2015 https://sites.google.com/site/ijcsis/ ISSN 1947-5500

A Margin-based Face Liveness Detection with Behavioral Confirmation

2021

This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author’s own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.

An Overview of Face Liveness Detection

International Journal on Information Theory, 2014

Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.

Face Liveness Detection : An Overview

International Journal of Scientific Research in Science and Technology, 2021

As the world becomes more and more digitized, the threat to security grows at an alarming rate. The mass usage of technology has garnered the attention and curiosity of people with foul intentions, whose aim is to exploit this use of technology to commit theft and other heinous crimes. One such technology used for security purposes is “Facial Recognition”. Face recognition is a popular biometric technique. Face recognition technology has advanced fast in recent years, and when compared to other ways, it is more direct, user-friendly, and convenient. Face recognition systems, on the other hand, are vulnerable to spoof assaults by non-real faces. To protect against spoofing, a secure system requires liveness detection. This study examines researchers' attempts to address the problem of spoofing and liveness detection, including mapping the research overview from the literature survey into a suitable taxonomy, exploring the fundamental properties of the field, motivation for using liveness detection methods in face recognition, and problems that may limit the benefits.

IRJET- Face Liveness Detection using Machine Learning and Neural Network -Literature Survey

IRJET, 2020

As the world becomes more and more digitized, the threat to security grows at an alarming rate. The mass usage of technology has garnered the attention and curiosity of people with foul intentions, whose aim is to exploit this use of technology to commit theft and other heinous crimes. One such technology used for security purposes is "Facial Recognition". And there are external forces who take advantage of the vulnerabilities of this technology by "Face Spoofing". This paper aims to elaborate the various techniques in face liveness detection. These techniques will enable the creation of a system which will be able to properly distinguish between a real and a fake face and thus limit the vulnerabilities of the face recognition system leading to a better level of security wherever face recognition is used.

A Patch-Based CNN Built on the VGG-16 Architecture for Real-Time Facial Liveness Detection

Sustainability

Facial recognition is a prevalent method for biometric authentication that is utilized in a variety of software applications. This technique is susceptible to spoofing attacks, in which an imposter gains access to a system by presenting the image of a legitimate user to the sensor, hence increasing the risks to social security. Consequently, facial liveness detection has become an essential step in the authentication process prior to granting access to users. In this study, we developed a patch-based convolutional neural network (CNN) with a deep component for facial liveness detection for security enhancement, which was based on the VGG-16 architecture. The approach was tested using two datasets: REPLAY-ATTACK and CASIA-FASD. According to the results, our approach produced the best results for the CASIA-FASD dataset, with reduced HTER and EER scores of 0.71% and 0.67%, respectively. The proposed approach also produced consistent results for the REPLAY-ATTACK dataset while maintaini...

Face Liveness Detection Competition (LivDet-Face) - 2021

2021 IEEE International Joint Conference on Biometrics (IJCB), 2021

Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions aga...

Survey of Various Face Liveness Detection Techniques for Biometric Antispoofing Applications

International Journal Of Engineering And Computer Science, 2017

Liveness control in face recognition with deep learning methods (original) (raw)

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