Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification

Abstract

Attributes, or mid-level semantic features, have gained popularity in the past few years in domains ranging from activity recognition to face verification. Improving the accuracy of attribute classifiers is an important first step in any application which uses these attributes. In most works to date, attributes have been considered independent of each other. However, attributes can be strongly related, such as heavy makeup and wearing lipstick as well as male and goatee and many others. We propose a multi-task deep convolutional neural network (MCNN) with an auxiliary network at the top (AUX) which takes advantage of attribute relationships for improved classification. We call our final network MCNN-AUX. MCNN-AUX uses attribute relationships in three ways: by sharing the lowest layers for all attributes, by sharing the higher layers for spatially-related attributes, and by feeding the attribute scores from MCNN into the AUX network to find score-level relationships. Using MCNN-AUX rather than individual attribute classifiers, we are able to reduce the number of parameters in the network from 64 million to fewer than 16 million and reduce the training time by a factor of 16. We demonstrate the effectiveness of our method by producing results on two challenging publicly available datasets achieving state-of-the-art performance on many attributes.