CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval

We propose a novel Coupled Projection multi-task Met- ric Learning (CP-mtML) method for large scale face re- trieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more chal- lenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face im- age datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary Pattern (LBP) de- scriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demon- strate the scalability and improved performance of the pro- posed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.