Face recognition performance comparison between fake faces and live faces

Cho, Miyoung; Jeong, Yoosoo

doi:10.1007/s00500-015-2019-4

Cited by 14 publications

(11 citation statements)

References 14 publications

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“…Generally, this is called a face recognition system and refers to the software part that realizes face recognition. Cho and Jeong [ 13 ] proposed a semiautomatic face recognition system model and feature extraction method, which aroused the interest of many practitioners, experts, and scholars, and it has also aroused the interest of the school teaching management reform personnel and researchers. Ozyurt and Ozyurt [ 14 ] summarized the biological structure, function, and working principle of neuron cells and realized neuron cells with mathematical model abstract simulation.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

Zhu

2021

Computational Intelligence and Neuroscience

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The basic idea of face recognition technology is to compare the matching degree between the standard face image marked with identity information and the static or dynamic face collected from the actual scene, which includes two main research contents: face feature extraction and face feature recognition. Traditional identification generally proves who we are through certificates, passwords, or certificates plus passwords. With the development of science and technology, face recognition technology will occupy an increasingly important position. Inspired by the human brain, the artificial neural network (ANN) is an information extraction system based on imitating the basic function and structure of the human brain and abstracted by physical and mathematical research methods. Based on the traditional BP neural network model, this paper proposes an ant colony algorithm-enabled BP neural network (ACO-BPNN) model and applies it to face recognition. Experimental results show that, similar to other face recognition techniques, the facial feature location needs to adapt to various changes of faces to the maximum extent, so the recognition and classification effect of the whole face feature extracted from the whole face image on the changes of such partial areas is not good, while the local feature extraction method based on ACO-BPNN can achieve a good recognition and classification effect.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

Zhu

2021

Computational Intelligence and Neuroscience

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“…In the previous works, we have introduced performance evaluation method of face recognition using face images from a high definition monitor and prove similarity between real faces and face images [5] [11]. However, the previous work has a limitation to reflect performance in real environments as it is a test only using frontal pose images.…”

Section: Previous Workmentioning

confidence: 99%

Face Recognition Performance Comparison Between Real Faces and Pose Variant Face Images from Image Display Device

Cho

Jeong

2015

Information and Communication Technology

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Part 8: Multimedia SecurityInternational audienceFace recognition technology, unlike other biometric methods, is conveniently accessible with the use of only a camera. Consequently, it has created an enormous interest in a variety of applications, including face identification, access control, security, surveillance, smart cards, law enforcement, human computer interaction. However, face recognition system is still not robust enough, especially in unconstrained environments, and recognition accuracy is still not acceptable. In this paper, to measure performance reliability of face recognition systems, we expand performance comparison test between real faces and face images from the recognition perspective and verify the adequacy of performance test methods using an image display device

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“…To avoid such problems, the authentication system requires that the image to be processed is captured from real faces, not other prostheses. Thus the living target identification has become one of the well-studied problems in computer vision [1]. ID:p0075 Some effective face recognition algorithms have been studied.…”

Section: Introductionmentioning

confidence: 99%

“…ID:p0075 Some effective face recognition algorithms have been studied. For face recognition, in [2,3], an original face image is converted into a corresponding sketch image, after which recognition is conducted by sketches. Certain handcrafted descriptors, such as local binary pattern (LBP), scale invariant feature transform (SIFT), and histogram of oriented gradients (HOG) [4][5][6] has the effect of comparing faces.…”

Section: Introductionmentioning

confidence: 99%

Living Face Verification via Multi-CNNs

Li¹,

Xie²,

Yan³

et al. 2018

IJCIS

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In face verification applications, precision rate and identifying liveness are two key factors. Traditional methods usually recognize global faces and can not gain good enough results when the faces are captured from different ages, or there are some interference factors, such as facial shade, etc. Besides, the false face attack will pose a great security risk. To solve the above problems, this study examines how to achieve reliable living face verification based on Multi-CNNs (convolutional neural networks) and Bayes probability. Participants are required to make several expressions in random orders and contents, to ensure their liveness. Then, an effective component-based method is proposed for face recognition, and synthesized multiple CNNs can help reflecting intensities of different components. Eventually, the similarity between faces is calculated by integrating the results of each CNN, with the help of Bayes probability. Comparative experiments demonstrate that our algorithm outperforms traditional methods in face recognition accuracy. Moreover, our algorithm has unique preponderance in that it can verify the liveness of users, which can achieve higher security.

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Face recognition performance comparison between fake faces and live faces

Cited by 14 publications

References 14 publications

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

Face Recognition Performance Comparison Between Real Faces and Pose Variant Face Images from Image Display Device

Living Face Verification via Multi-CNNs

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