Face retriever: Pre-filtering the gallery via deep neural net

Abstract

Face retrieval is an enabling technology for many applications, including automatic face annotation, deduplication, and surveillance. In this paper, we propose a face retrieval system which combines a k-NN search procedure with a COTS matcher (PittPatt1) in a cascaded manner. In particular, given a query face, we first pre-filter the gallery set and find the top-k most similar faces for the query image by using deep facial features that are learned with a deep convolutional neural network. The top-k most similar faces are then re-ranked based on score-level fusion of the similarities between deep features and the COTS matcher. To further boost the retrieval performance, we develop a manifold ranking algorithm. The proposed face retrieval system is evaluated on two large-scale face image databases: (i) a web face image database, which consists of over 3, 880 query images of 1, 507 subjects and a gallery of 5, 000, 000 faces, and (ii) a mugshot database, which consists of 1, 000 query images of 1, 000 subjects and a gallery of 1, 000, 000 faces. Experimental results demonstrate that the proposed face retrieval system can simultaneously improve the retrieval performance (CMC and precision-recall) and scalability for large-scale face retrieval problems.