Noise robust face morphing detection method

Le-Bing Zhang; Juan Cai; Fei Peng; Min Long; Yuanquan Shi

doi:10.1117/12.2628711

22 April 2022 Noise robust face morphing detection method

Le-Bing Zhang, Juan Cai, Fei Peng, Min Long, Yuanquan Shi

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 1217417 (2022) https://doi.org/10.1117/12.2628711
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Face morphing attack has become a severe threat to the current face recognition systems. Though there are some methods for detecting face morphing, the performance of these methods is susceptible to noise. Aiming to enhance the performance of resisting noise in face morphing detection, a noise robust convolutional neural network is proposed in this paper. The structure of the network is divided into two parts: facial image adaptive denoising and face morphing detection. Before the face morphing detection, the auto-encoders are first utilized to adaptively denoise the noised facial images, which can effectively reduce the influence of noise on face morphing detection. Then, the pre-trained VGG19 convolution neural network with powerful classification ability is used for face morphing detection with the generated noise-free facial images. Experimental results indicate that the proposed method can effectively reduce the noise influence on face morphing detection, and can achieve better performance compared with some existing methods.

Citation Download Citation

Le-Bing Zhang, Juan Cai, Fei Peng, Min Long, and Yuanquan Shi "Noise robust face morphing detection method", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 1217417 (22 April 2022); https://doi.org/10.1117/12.2628711

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