Scalable Face Retrieval by Simple Classifiers and Voting Scheme

Abstract

In this paper, we propose a scalable face retrieval method on large data. In order to search a particular person from videos on the Web, face recognition is one of the most effective methods. Needless to say that retrieving faces from videos are more challenging than that from a still image due to inconsistency in imaging conditions such as change of view point, lighting condition and resolution. However, dealing with them is not enough to realize the retrieval on large data. In addition, a face recognition method on the videos should be highly scalable as the number of the videos on the Web is enormous. Existing face recognition methods do not scale. In order to realize scalable face recognition, we propose a novel face recognition method. The proposed method is scalable even if the data is million-scale with high accuracy. The proposed method uses local features for face representation, and an approximate nearest neighbor (ANN) search for feature matching to reduce computational time. A voting scheme is used for recognition to compensate for low accuracy of the ANN search. We created a 5 million database and evaluated the proposed method. As results, the proposed method showed more than thousand times faster than a conventional sublinear method. Moreover, the proposed method recognized face images with a top 1000 cumulative accuracy of 100% in 139 ms recognition time (excluding preprocessing and feature extraction for the query image) per query image on the 5 million face database.