  Deep Neural Network (DNN) has recently achieved outstanding performance in a
variety of computer vision tasks, including facial attribute classification.
The great success of classifying facial attributes with DNN often relies on a
massive amount of labelled data. However, in real-world applications, labelled
data are only provided for some commonly used attributes (such as age, gender);
whereas, unlabelled data are available for other attributes (such as
attraction, hairline). To address the above problem, we propose a novel deep
transfer neural network method based on multi-label learning for facial
attribute classification, termed FMTNet, which consists of three sub-networks:
the Face detection Network (FNet), the Multi-label learning Network (MNet) and
the Transfer learning Network (TNet). Firstly, based on the Faster Region-based
Convolutional Neural Network (Faster R-CNN), FNet is fine-tuned for face
detection. Then, MNet is fine-tuned by FNet to predict multiple attributes with
labelled data, where an effective loss weight scheme is developed to explicitly
exploit the correlation between facial attributes based on attribute grouping.
Finally, based on MNet, TNet is trained by taking advantage of unsupervised
domain adaptation for unlabelled facial attribute classification. The three
sub-networks are tightly coupled to perform effective facial attribute
classification. A distinguishing characteristic of the proposed FMTNet method
is that the three sub-networks (FNet, MNet and TNet) are constructed in a
similar network structure. Extensive experimental results on challenging face
datasets demonstrate the effectiveness of our proposed method compared with
several state-of-the-art methods.
