Facial Image Analysis by CNN with Weighted Heterogeneous Learning
- Hiroshi Fukui, Takayoshi Yamashita, Yuu Kato, Ryo Matsui, Yuji Yamauchi, Hironobu Fujiyoshi
- International Workshop on Advanced Image Technology, 2017
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Recognition of facial attributes such as facial point, gender, and age have been used in marketing strategies and client services on social networks. In general, to recognize these attributes, it requires independent handcraft features and classifiers for each task. Heterogeneous learning is able to train a single classifier to perform multiple tasks. This learning method simultaneously train regression and recognition tasks, thereby reducing both training and testing time. However, differences between training error negatively affect the training process in specific tasks. To address this problem. we propose weighted heterogeneous learning which has weighed error function for a deep convolutional neural network. Our method outperformed the conventional method in terms of facial attribute recognition, especially for regression tasks such as facial point detection, age estimation, and smile ratio estimation.
※本発表はIWAIT2017 Best Paper Awardを受賞しました。