Adaptive Mean-Residue Loss for Robust Facial Age Estimation

Autor:	Zhao, Ziyuan, Qian, Peisheng, Hou, Yubo, Zeng, Zeng
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Image and Video Processing
Zdroj:	2022 IEEE International Conference on Multimedia and Expo (ICME)
Druh dokumentu:	Working Paper
DOI:	10.1109/ICME52920.2022.9859703
Popis:	Automated facial age estimation has diverse real-world applications in multimedia analysis, e.g., video surveillance, and human-computer interaction. However, due to the randomness and ambiguity of the aging process, age assessment is challenging. Most research work over the topic regards the task as one of age regression, classification, and ranking problems, and cannot well leverage age distribution in representing labels with age ambiguity. In this work, we propose a simple yet effective loss function for robust facial age estimation via distribution learning, i.e., adaptive mean-residue loss, in which, the mean loss penalizes the difference between the estimated age distribution's mean and the ground-truth age, whereas the residue loss penalizes the entropy of age probability out of dynamic top-K in the distribution. Experimental results in the datasets FG-NET and CLAP2016 have validated the effectiveness of the proposed loss. Our code is available at https://github.com/jacobzhaoziyuan/AMR-Loss. Comment: Accepted by IEEE International Conference on Multimedia and Expo (ICME 2022)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.17156 Zobrazit plný text záznamu View this record from Arxiv