Facial landmark detection algorithm based on depth structure model

Dawei Yang; Liyan Bao; Hongli Yao

doi:10.1117/12.2675281

25 May 2023 Facial landmark detection algorithm based on depth structure model

Dawei Yang, Liyan Bao, Hongli Yao

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126361P (2023) https://doi.org/10.1117/12.2675281
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In order to solve the problem that the existing face key point detection methods based on depth learning do not explicitly embed the structural dependency between face key points, and most networks have high memory access costs and energy consumption, this paper proposes a depth structure face key point detection method combining depth convolution neural network and all connected conditional random field. This method uses an efficient VoVNet depth neural network to replace the previous CNN model, reduce the memory access cost and energy consumption, and capture the structural relationship changes caused by posture and deformation. The experimental results show that this method is superior to the previous methods in face key point detection, the access cost is greatly increased, and it has good generalization ability for challenging data sets.

Citation Download Citation

Dawei Yang, Liyan Bao, and Hongli Yao "Facial landmark detection algorithm based on depth structure model", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126361P (25 May 2023); https://doi.org/10.1117/12.2675281

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