Paper Info
Title
Robust gender recognition for real-time surveillance system
Abstract
Gender recognition is a challenging task in surveillance videos due to their relatively low-solution, uncontrolled environment and viewing angles of human subject. In this work, a surveillance system of real-time gender recognition is developed. The contribution of this work is four-fold. In order to make the system robust, a mechanism of decision making based on the combination of neighboring face detection, context-regions enhancement and confidence-based weighting assignment is designed. Considering the spatiotemporal consistency of the gender between consecutive faces in successive frames, the belief propagation is employed to model the temporal coherence. Experiment results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders in real-time surveillance videos.
Year
DOI
Venue
2010
10.1109/ICME.2010.5583879
ICME
Keywords
Field
DocType
face detection,temporal coherence,real-time surveillance system,face recognition,belief propagation,decision making,real-time system,real-time gender recognition,context-region enhancement,spatiotemporal consistency,belief maintenance,gender recognition,uncontrolled environment,object detection,confidence-based weighting assignment,image enhancement,robust gender recognition,video surveillance,real time,face,detectors,real time systems,feature extraction,real time system
Object detection,Facial recognition system,Computer vision,Weighting,Pattern recognition,Computer science,Feature extraction,Real-time operating system,Coherence (physics),Artificial intelligence,Face detection,Belief propagation
Conference
ISSN
ISBN
Citations 
1945-7871
978-1-4244-7491-2
5
PageRank 
References 
Authors
0.40
14
2
Name
Order
Citations
PageRank
Duan-Yu Chen129628.79
Kuan-Yi Lin261.77