Face spoofing detection model based on multi-scale predictive feature fusion

Ling Huang; Xi Ping He; Dan He

doi:10.1117/12.2681169

8 June 2023 Face spoofing detection model based on multi-scale predictive feature fusion

Ling Huang, Xi Ping He, Dan He

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127071F (2023) https://doi.org/10.1117/12.2681169
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Face spoofing detection technology can accurately distinguish the truth of the face captured by the camera, and it has been applied in many security fields. In view of the problems of the existing face spoofing detection models, such as the single way to extract image features and the insufficient semantic information, a model architecture that fused the predictive feature maps of different scales was proposed. Firstly, the face image is randomly cropped and the local image is taken as input; Then, a cheap feature predictive convolution attention module is designed to alleviate the problem of feature map redundancy, and make full use of the similarity features of adjacent blocks around local pixels, that is, predict the feature value of the central region through surrounding pixels; Finally, the feature maps extracted from different levels are fused. The model aims to achieve high accuracy in identifying the authenticity of face images. The experiments show that the accuracy rate on CASIA-SURF (Depth modal) dataset is 99.42%, the average classification error rate is 0.53%, and the zero error rate is achieved on CASIA-FASD and Replay-Attack datasets. The model parameter quantity is only 0.37M, which is lower than most of the model architecture based on convolution neural networks.

Citation Download Citation

Ling Huang, Xi Ping He, and Dan He "Face spoofing detection model based on multi-scale predictive feature fusion", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127071F (8 June 2023); https://doi.org/10.1117/12.2681169

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KEYWORDS

Feature extraction

Data modeling

Facial recognition systems

Convolution

Feature fusion

Tunable filters

Semantics

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