Low Resolution Gait Recognition with High Frequency Super Resolution

Authors:
Junping Zhang;Yuan Cheng;Changyou Chen
Affiliations:
Shanghai Key Laboratory of Intelligent Information Processing, and Department of Computer Science and Engineering, Fudan university, Shanghai, China 200433;Department of Computer Science and Engineering, Fudan university, Shanghai, China 200433;Department of Computer Science and Engineering, Fudan university, Shanghai, China 200433
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

Being non-invasive and effective at a distance, recognition suffers from low resolution sequence case. In this paper, we attempt to address the issue through the proposed high frequency super resolution method. First, a group of high resolution training gait images are degenerated for capturing high-frequency information loss. Then the combination of neighbor embedding with interpolation methods is employed for learning and recovering a high resolution test image from low resolution counterpart. Finally, classification is performed based on nearest neighbor classifier. The experiment indicates that the proposed method can effectively improve the accuracy of gait recognition under low resolution case.