Multi-Attribute Recognition of Facial Images Considering Exclusive and Correlated Relationship Among Attributes

Multi-attribute recognition is one of the main topics attaining much attention in the pattern recognition field these days. The conventional approaches to multi-attribute recognition has mainly focused on developing an individual classifier for each attribute. However, due to rapid growth of deep learning techniques, multi-attribute recognition using multi-task learning enables the simultaneous recognition of more than two relevant recognition tasks through a single network. A number of studies on multi-task learning have shown that it is effective in improving recognition performance for all tasks when related tasks are learned together. However, since there are no specific criteria for determining the relationship among attributes, it is difficult and confusing to choose a good combination of tasks that have a positive impact on recognition performance. As one way to solve this problem, we propose a multi-attribute recognition method based on the novel output representations of a deep learning network which automatically learns the exclusive and joint relationship among attribute recognition tasks. We apply our proposed method to multi-attribute recognition of facial images, and confirm the effectiveness through experiments on a benchmark database.