Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Automatic gait recognition using area-based metrics
Pattern Recognition Letters
Extracting a diagnostic gait signature
Pattern Recognition
Extracting a diagnostic gait signature
Pattern Recognition
Fusing gait and face cues for human gender recognition
Neurocomputing
Gender Recognition Based on Fusion of Face and Multi-view Gait
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Analyzing Human Gait Using Patterns of Translation and Rotation
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
A study on gait-based gender classification
IEEE Transactions on Image Processing
Gender recognition from gait using radon transform and relevant component analysis
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
Human attributes from 3D pose tracking
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Gait analysis of gender and age using a large-scale multi-view gait database
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Gender classification using a novel gait template: radon transform of mean gait energy image
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Person de-identification in videos
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Human attributes from 3D pose tracking
Computer Vision and Image Understanding
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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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.