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We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML)method for large scale face retrieval. In contrast to previous works which werelimited to low dimensional features and small datasets, the proposed methodscales to large datasets with high dimensional face descriptors. It utilisespairwise (dis-)similarity constraints as supervision and hence does not requireexhaustive class annotation for every training image. While, traditionally,multi-task learning methods have been validated on same dataset but differenttasks, we work on the more challenging setting with heterogeneous datasets anddifferent tasks. We show empirical validation on multiple face image datasetsof different facial traits, e.g. identity, age and expression. We use classicLocal Binary Pattern (LBP) descriptors along with the recent Deep ConvolutionalNeural Network (CNN) features. The experiments clearly demonstrate thescalability and improved performance of the proposed method on the tasks ofidentity and age based face image retrieval compared to competitive existingmethods, on the standard datasets and with the presence of a million distractorface images.

CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval