Semi-supervised face image retrieval using sparse coding with identity constraint

We aim to develop a scalable face image retrieval system which can integrate with partial identity information to improve the retrieval result. To achieve this goal, we first apply sparse coding on local features extracted from face images combining with inverted indexing to construct an efficient and scalable face retrieval system. We then propose a novel coding scheme that refines the representation of the original sparse coding by using identity information. Using the proposed coding scheme, face images with large intra-class variances will still be quantized into similar visual words if they share the same identity. Experimental results show that our system can achieve salient retrieval results on LFW dataset (13K faces) and outperform linear search methods using well known face recognition feature descriptors.