Improving large-scale face image retrieval using multi-level features

In recent years, extensive efforts have been made for face recognition and retrieval systems. However, there remain several challenging tasks for face image retrieval in unconstrained databases where the face images were captured with varying poses, lighting conditions, etc. In addition, the databases are often large-scale, which demand efficient retrieval algorithms that have the merit of scalability. To improve the retrieval accuracy of the face images with different poses and imaging characteristics, we introduce a novel feature extraction method to bag-of-words (BoW) based face image retrieval system. It employs various scales of features simultaneously to encode different texture information and emphasizes image patches that are more discriminative as parts of the face. Moreover, the overlapping image patches at different scales compensate for the pose variation and face misalignment. Experiments conducted on a large-scale public face database demonstrate the superior performance of the proposed approach compared to the state-of-the-art method.