상세정보
학술대회
Gender Classification in Human Gait Using Support Vector Machine
- 저자
- 유장희, 황두성, Mark S. Nixon
- 발행일
- 200509
- 출처
- International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS) 2005 (LNCS 3708), v.3708, pp.138-145
- 협약과제
- 05MK2300, 고인식 다중생체인식 전용칩셋 기술, 정교일
- 초록
- We describe an automated system that classifies gender by utilising a set of human gait data. The gender classification system consists of three stages: i) detection and extraction of the moving human body and its contour from image sequences; ii) extraction of human gait signature by the joint angles and body points; and iii) motion analysis and feature extraction for classifying gender in the gait patterns. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature generation based on motion parameters. Then, an SVM classifier is used to classify gender in the gait patterns. In experiments, higher gender classification performances, which are 96% for 100 subjects, have been achieved on a considerably larger database. © Springer-Verlag Berlin Heidelberg 2005.
- KSP 제안 키워드
- Classification system, Detection and extraction, Feature extractioN, Feature generation, Image sequence, Joint angles, Motion parameters, SVM Classifier, Support VectorMachine(SVM), an automated system, gait patterns