A Three-stage Filtering Approach for Face Recognition

Face recognition has become a crucial topic in recent decades, which offers important opportunities for applications in security surveillance, human-computer interaction, and forensics. However, it poses challenges, including uncontrolled environments, large datasets, and insufficiency of training data. In this paper, a face recognition system is proposed to iron out the above problems with a new framework based on a hashing function in a three-stage filtering approach. At the first stage, candidate subjects are chosen using the Locality-Sensitive Hashing (LSH) function. We employ a voting system to select candidates via disregarding a large number of dissimilar identities considering their local features. At the second stage, a robust image hashing based on Discrete Cosine Transform (DCT) coefficients is used to further refine the candidate images in terms of global visual information. Finally, the test image is recognized among selected identities using other visual information, resulting in further accuracy gains. Extensive experiments on FERET, AR, and ORL datasets show that the proposed method outperforms with a significant improvement in accuracy over the state-of-the-art methods.