著者

Takayoshi Yamashita Takaya Nakamura Hiroshi Fukui Yuji Yamauchi Hironobu Fujiyoshi

出版者

一般社団法人 情報処理学会

雑誌

IPSJ Transactions on Computer Vision and Applications (ISSN:18826695)

巻号頁・発行日

vol.7, pp.99-103, 2015 (Released:2015-07-27)

参考文献数

15

被引用文献数

6

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Facial part labeling which is parsing semantic components enables high-level facial image analysis, and contributes greatly to face recognition, expression recognition, animation, and synthesis. In this paper, we propose a cost-alleviative learning method that uses a weighted cost function to improve the performance of certain classes during facial part labeling. As the conventional cost function handles the error in all classes equally, the error in a class with a slightly biased prior probability tends not to be propagated. The weighted cost function enables the training coefficient for each class to be adjusted. In addition, the boundaries of each class may be recognized after fewer iterations, which will improve the performance. In facial part labeling, the recognition performance of the eye class can be significantly improved using cost-alleviative learning.