Face anti-spoofing algorithm combined with CNN and brightness equalization

Cai, Pei; Quan, Huimin

doi:10.1007/s11771-021-4596-y

Cited by 11 publications

(7 citation statements)

References 19 publications

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Section: Resultsmentioning

confidence: 99%

“…We compared with the methods proposed in [22,27,28,29,30,31], and the results are shown in Table 1. The experimental results show that compared with the methods proposed in [22,27,28,29,30,31], the EER and HTER indexes of the method we proposed are the lowest on the data set. The model has obvious advantages in generalization and robustness against unknown attacks, and effectively avoids the common in class overfitting problem in the face detection task.…”

Section: Resultsmentioning

confidence: 99%

“…Compared with this method, EER decreases by about 1.73%, HTER decreases by about 1.76% on CASIA-SURF dataset, and HTER decreases by about 10.74% in cross dataset test. [31] proposed a method combining convolution neural network with brightness equalization, which uses brightness equalization to compensate the brightness of different brightness regions of the face image, and then uses convolution network to extract features and classify them. Compared with the CASIA-SURF data set, the EER and HTER of this method are reduced by about 2.63%, 2.67% and 9.42% respectively.…”

Section: Resultsmentioning

confidence: 99%

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Multi-channel face liveness detection based on multi-scale feature fusion

Wang

Tang²

2023

International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022)

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A multi-channel face liveness detection method based on multi-scale feature fusion is proposed to solve the problems of poor stability, poor generalization, and poor robustness against unknown attacks of existing face liveness detection models. Firstly, the method uses a multichannel residual network and introduces the center differential convolution and SimAM attention module in the residual block to improve the feature extraction ability and stability of the model. Secondly, the information contained in the feature map at different scales is further mined by multiscale feature fusion at the end of each channel. Finally, the network is supervised by using cross modal focal loss as an aid to binary cross entropy loss. Extensive evaluations in two publicly available datasets demonstrate the effectiveness, generalization, and robustness of the proposed method against unknown attacks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Multi-channel face liveness detection based on multi-scale feature fusion

Wang

Tang²

2023

International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022)

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“…Humans may be distinguished from inanimate items like images by this movement. Changes in face expression, blinking eyes, and lip motions are a few of the most often used motion detection methods [6]. Motion-based evaluation methods are typically adequate for preventing inactive representation strikes such as photo-spoofing, but they fail to prevent dynamic rendering attacks such as videos [7].…”

Section: Introductionmentioning

confidence: 99%

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

Shinde,

Raundale

2024

IJ-AI

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<p>In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.</p>

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“…An impostor can gain access to the system by presenting a copy of the image to the camera. Therefore, prior to face recognition authentication, face liveness detection should be implemented to detect whether the captured face is live or fake [5]. To address face spoofing attacks, researchers have proposed different methods for face liveness detection, such as the use of the enhanced local binary pattern (LBP), motion analysis, texture analysis, and quality analysis of captured images, among others.…”

Section: Introductionmentioning

confidence: 99%

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

Yang¹,

Machica²,

Dumdumaya³

et al. 2022

IJETAE

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—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 LeNet5 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

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Face anti-spoofing algorithm combined with CNN and brightness equalization

Cited by 11 publications

References 19 publications

Multi-channel face liveness detection based on multi-scale feature fusion

Multi-channel face liveness detection based on multi-scale feature fusion

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

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

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