Multi-Objective Convolutional Learning for Face Labeling

Sifei Liu, Jimei Yang, Chang Huang, Ming-Hsuan Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3451-3459

Abstract


This paper formulates face labeling as a conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Moreover, we regularize the network by using a nonparametric prior as new input channels in addition to the RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments on both the LFW and Helen datasets demonstrate state-of-the-art results of the proposed algorithm, and accurate labeling results on challenging images can be obtained by the proposed algorithm for real-world applications.

Related Material


[pdf]
[bibtex]
@InProceedings{Liu_2015_CVPR,
author = {Liu, Sifei and Yang, Jimei and Huang, Chang and Yang, Ming-Hsuan},
title = {Multi-Objective Convolutional Learning for Face Labeling},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}