Paper Info
Title
Benchmarking still-to-video face recognition via partial and local linear discriminant analysis on COX-S2V dataset
Abstract
In this paper, we explore the real-world Still-to-Video (S2V) face recognition scenario, where only very few (single, in many cases) still images per person are enrolled into the gallery while it is usually possible to capture one or multiple video clips as probe. Typical application of S2V is mug-shot based watch list screening. Generally, in this scenario, the still image(s) were collected under controlled environment, thus of high quality and resolution, in frontal view, with normal lighting and neutral expression. On the contrary, the testing video frames are of low resolution and low quality, possibly with blur, and captured under poor lighting, in non-frontal view. We reveal that the S2V face recognition has been heavily overlooked in the past. Therefore, we provide a benchmarking in terms of both a large scale dataset and a new solution to the problem. Specifically, we collect (and release) a new dataset named COX-S2V, which contains 1,000 subjects, with each subject a high quality photo and four video clips captured simulating video surveillance scenario. Together with the database, a clear evaluation protocol is designed for benchmarking. In addition, in addressing this problem, we further propose a novel method named Partial and Local Linear Discriminant Analysis (PaLo-LDA). We then evaluated the method on COX-S2V and compared with several classic methods including LDA, LPP, ScSR. Evaluation results not only show the grand challenges of the COX-S2V, but also validate the effectiveness of the proposed PaLo-LDA method over the competitive methods.
Year
DOI
Venue
2012
10.1007/978-3-642-37444-9_46
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
Field
DocType
face recognition scenario,video clip,competitive method,video surveillance scenario,classic method,high quality photo,testing video frame,multiple video clip,cox-s2v dataset,high quality,s2v face recognition,local linear discriminant analysis,still-to-video face recognition
Computer vision,Facial recognition system,Pattern recognition,Computer science,Grand Challenges,Artificial intelligence,Linear discriminant analysis,Machine learning,Benchmarking,CLIPS
Conference
Volume
Issue
ISSN
7725 LNCS
PART 2
16113349
Citations 
PageRank 
References 
21
0.72
23
Authors
6
Name
Order
Citations
PageRank
Zhiwu Huang125215.26
Shiguang Shan26322283.75
Haihong Zhang33489.17
Shihong Lao42005118.22
Alifu Kuerban5441.45
Xilin Chen66291306.27