Toward Large-Population Face Identification in Unconstrained Videos

We investigate large-scale face identification in unconstrained videos with 1000 subjects. This problem is very challenging, and until now most studies have only considered the scenarios with a small number of subjects and videos captured in controlled laboratory environments. Our contributions in this paper are twofold. First, we set up a large-scale video database in an unconstrained environment, Celebrity-1000, with data collected from two popular video-sharing websites, YouTube and Youku, for face identification research. It contains 1000 celebrities from different countries, ~7000 videos, ~160 K tracking sequences, and ~2.4 M sampled frames. Second, we boost the efficiency of multitask joint sparse representation (MTJSR) algorithm for video-based face identification on Celebrity-1000. MTJSR is training free and can naturally integrate multiple frames of the same tracking sequence for collaborative inference, and thus is suitable for video-based face identification. We present a sparsity-induced scalable optimization method, which solves the large-scale MTJSR problem by sequentially solving a series of smaller-scale subproblems with theoretically guaranteed convergency. Extensive experiments show several orders-of-magnitude speedup with this new optimization method, and also demonstrate the superiorities of the accelerated MTJSR algorithm over several popular baseline algorithms.