Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval
Descriptors such as local binary patterns perform well for face recognition. Searching large databases using such descriptors has been problematic due to the cost of the linear search, and the inadequate performance of existing indexing methods. We present Discrete Cosine Transform (DCT) hashing for creating index structures for face descriptors. Hashes play the role of keywords: an index is created, and queried to find the images most similar to the query image. Common hash suppression is used to improve retrieval efficiency and accuracy. Results are shown on a combination of six publicly available face databases (LFW, FERET, FEI, BioID, Multi-PIE, and RaFD). It is shown that DCT hashing has significantly better retrieval accuracy and it is more efficient compared to other popular state-of-the-art hash algorithms.