Face Recognition in Movie Trailers via Mean Sequence Sparse Representation-Based Classification

Enrique G. Ortiz, Alan Wright, Mubarak Shah; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3531-3538

Abstract


This paper presents an end-to-end video face recognition system, addressing the difficult problem of identifying a video face track using a large dictionary of still face images of a few hundred people, while rejecting unknown individuals. A straightforward application of the popular n-minimization for face recognition on a frame-by-frame basis is prohibitively expensive, so we propose a novel algorithm Mean Sequence SRC (MSSRC) that performs video face recognition using a joint optimization leveraging all of the available video data and the knowledge that the face track frames belong to the same individual. By adding a strict temporal constraint to the ii-minimization that forces individual frames in a face track to all reconstruct a single identity, we show the optimization reduces to a single minimization over the mean of the face track. We also introduce a new Movie Trailer Face Dataset collected from 101 movie trailers on YouTube. Finally, we show that our method matches or outperforms the state-of-the-art on three existing datasets (YouTube Celebrities, YouTube Faces, and Buffy) and our unconstrained Movie Trailer Face Dataset. More importantly, our method excels at rejecting unknown identities by at least 8% in average precision.

Related Material


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[bibtex]
@InProceedings{Ortiz_2013_CVPR,
author = {Ortiz, Enrique G. and Wright, Alan and Shah, Mubarak},
title = {Face Recognition in Movie Trailers via Mean Sequence Sparse Representation-Based Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}