8 research outputs found
Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification
Facial Attribute Classification (FAC) has attracted increasing attention in
computer vision and pattern recognition. However, state-of-the-art FAC methods
perform face detection/alignment and FAC independently. The inherent
dependencies between these tasks are not fully exploited. In addition, most
methods predict all facial attributes using the same CNN network architecture,
which ignores the different learning complexities of facial attributes. To
address the above problems, we propose a novel deep multi-task multi-label CNN,
termed DMM-CNN, for effective FAC. Specifically, DMM-CNN jointly optimizes two
closely-related tasks (i.e., facial landmark detection and FAC) to improve the
performance of FAC by taking advantage of multi-task learning. To deal with the
diverse learning complexities of facial attributes, we divide the attributes
into two groups: objective attributes and subjective attributes. Two different
network architectures are respectively designed to extract features for two
groups of attributes, and a novel dynamic weighting scheme is proposed to
automatically assign the loss weight to each facial attribute during training.
Furthermore, an adaptive thresholding strategy is developed to effectively
alleviate the problem of class imbalance for multi-label learning. Experimental
results on the challenging CelebA and LFWA datasets show the superiority of the
proposed DMM-CNN method compared with several state-of-the-art FAC methods