Attributes for Improved Attributes

We introduce a method for improving facial attribute predictions using other attributes. In the domain of face recognition and verification, attributes are high-level descriptions of face images. Attributes are very useful for identification as well as image search as they provide easily understandable descriptions of faces, rather than most other image descriptors (i.e. HOG, LBP, and SIFT). A facial attribute is typically considered a binary variable: 0 meaning the face does not exhibit the attribute, and 1 meaning that it does. Work up to the present has considered all attributes of a face to be independent. However, we know that many face attributes are highly correlated, i.e. gender and facial hair. We propose to take advantage of these correlations to improve attribute classification. We study the attribute correlations in a very challenging face dataset, and demonstrate that both automatic correlation discovery and manual correlation rules result in an increase in classification for binary attributes. This is the first work to utilize the relationship amongst binary attributes for improved classification performance. Using a deep convolutional neural network for feature extraction and classification, along with our automatic correlation discovery method, we achieve state-ofthe-art results for attribute classification.