IPSJ Transactions on Computer Vision and Applications
Online ISSN : 1882-6695
ISSN-L : 1882-6695
Cost-alleviative Learning for Deep Convolutional Neural Network-based Facial Part Labeling
Takayoshi YamashitaTakaya NakamuraHiroshi FukuiYuji YamauchiHironobu Fujiyoshi
Author information

2015 Volume 7 Pages 99-103


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.

Information related to the author
© 2015 by the Information Processing Society of Japan
Previous article Next article