A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Fusing gait and face cues for human gender recognition
Neurocomputing
A study on gait-based gender classification
IEEE Transactions on Image Processing
Gender classification based on fusion of multi-view gait sequences
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multimodal facial gender and ethnicity identification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Hierarchical pose estimation for human gait analysis
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
We present an adaptive three-mode PCA framework for recognizing gender from walking movements. Prototype female and male walkers are initially decomposed into a sub-space of their three-mode components (posture, time, gender). We then assign an importance weight to each motion trajectory in the sub-space and have the model automatically learn the weight values (key features) from labeled training data. We present experiments of recognizing physical (actual) and perceived (from perceptual experiments) gender for 40 walkers. The model demonstrates greater than 90% recognition for both contexts and shows greater flexibility than standard PCA.