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  4. Vol 7, Issue 1, 2024

Lightweight Face Anti-spoofing for Improved MobileNetV3

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DOI: 10.23977/jipta.2024.070117 | Downloads: 36 | Views: 1295

Author(s)

Sun Zhenlin 1, Yan Hao 1, Guo Mengyu 1, Hao Zhiqiang 1

Affiliation(s)

1 School of Science, Tianjin University of Commerce, Tianjin, 300134, China

Corresponding Author

Sun Zhenlin

ABSTRACT

Aiming at the security challenges associated with facial authentication in biometric technology, we propose an improved MobileNetV3 model that reduces the complexity and cost of existing face anti-deception methods. This model integrates the Convolutional Block Attention Module (CBAM) and Central Difference Convolution (CDC) techniques. CBAM enhances feature representation, while CDC captures fine-grained information by aggregating intensity and gradient data. Experimental results from the NUAA and Replay-Attack datasets indicate that the improved model achieves a recognition accuracy exceeding 97% without a significant increase in computational requirements, highlighting its potential for mobile applications.

KEYWORDS

Face anti-spoofing, Convolutional block attention mechanism, Central difference convolution, Lightweight neural network

CITE THIS PAPER

Sun Zhenlin, Yan Hao, Guo Mengyu, Hao Zhiqiang, Lightweight Face Anti-spoofing for Improved MobileNetV3. Journal of Image Processing Theory and Applications (2024) Vol. 7: 143-151. DOI: http://dx.doi.org/10.23977/jipta.2024.070117.

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