상세정보
학술대회
Feature Extraction and Selection for Recognizing Humans by Their Gait
- 저자
- 유장희, Mark S. Nixon
- 발행일
- 200611
- 출처
- International Symposium on Visual Computing (ISVC) 2006 (LNCS 4292), v.4292, pp.156-165
- 협약과제
- 06MK1500, 고인식 다중생체인식 전용칩셋 기술, 정교일
- 초록
- We describe an efficient and effective feature extraction and selection method for identifying humans by their gait. A sequential set of 2D stick figures is extracted from gait silhouette data by determining the joint angles and body points, and it is used to represent the gait signature that is primitive data for extracting motion parameters. The motion parameters in the gait signatures are stride length, cycle time, speed, and joint angles, and the gait features are extracted from these motion parameters. By measuring a class separability of the extracted features, important features are selected from original feature sets for classifying human in the gait patterns. Then, a k-NN classifier is used to analyze the discriminatory ability of the selected features. In experiments, higher gait classification performances, which are 96.7%, have been achieved. © Springer-Verlag Berlin Heidelberg 2006.
- KSP 제안 키워드
- Class separability, Cycle Time, Feature extraction and selection, Feature set, Gait Classification, Joint angles, Motion parameters, Selected features, Selection method, Stride length, gait patterns