Authors:
Lionel Prevost
1
;
Philippe Phothisane
2
and
Erwan Bigorgne
2
Affiliations:
1
University of the French West Indies and Guiana, France
;
2
Eikeo, France
Keyword(s):
Face Analysis, Boosting, Gender Estimation, Age Estimation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Telecommunications
;
Video Analysis
Abstract:
Research has recently focused on human age and gender estimation because they are useful cues in many applications such as human-machine interaction, soft biometrics or demographic statistics for marketing. Even though human perception of other people’s age is often biased, attaining this kind of precision with an automatic estimator is still a difficult challenge. In this paper, we propose a real time face tracking framework that includes a sequential estimation of people’s gender then age. A single gender estimator and several gender-specific age estimators are trained using a boosting scheme and their decisions are combined to output a gender and an age in years. We choose to train all these estimators using local binary patterns histograms extracted from still facial images. The whole process is thoroughly tested on state-of art databases and video sets. Results on the popular FG-NET database show results comparable to human perception (overall 70% correct responses within 5 year
s tolerance and almost 90% within 10 years tolerance). The age and gender estimators can output decisions at 21 frames per second. Combined with the face tracker, they provide real-time estimations of age and gender.
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