Face Retriever: Pre-filtering the Gallery via Deep Neural Net
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, U.S.A.
IEEE ICB, 2015
@article{wang2015face,
title={Face Retriever: Pre-filtering the Gallery via Deep Neural Net},
author={Wang, Dayong and Jain, Anil K},
year={2015}
}
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 (PittPatt) 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.
March 30, 2015 by hgpu
