Robust gender recognition for real-time surveillance system

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.

[1]  W. Freeman,et al.  Generalized Belief Propagation , 2000, NIPS.

[2]  Volkan Atalay,et al.  PCA for gender estimation: which eigenvectors contribute? , 2002, Object recognition supported by user interaction for service robots.

[3]  Ming-Hsuan Yang,et al.  Gender classification with support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[4]  David Masip,et al.  Are External Face Features Useful for Automatic Face Classification? , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[5]  Javier Ruiz-del-Solar,et al.  Gender Classification of Faces Using Adaboost , 2006, CIARP.

[6]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[7]  M. Mayo,et al.  Improving face gender classification by adding deliberately misaligned faces to the training data , 2008, 2008 23rd International Conference Image and Vision Computing New Zealand.

[8]  Andrew C. Gallagher,et al.  Understanding images of groups of people , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Yuchun Fang,et al.  Improving LBP features for gender classification , 2008, 2008 International Conference on Wavelet Analysis and Pattern Recognition.

[10]  Pedro García-Sevilla,et al.  Gender Recognition from a Partial View of the Face Using Local Feature Vectors , 2009, IbPRIA.

[11]  Jordi Vitrià,et al.  Gender Recognition in Non Controlled Environments , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[12]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[13]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Hui Lin,et al.  Gender Recognition using Adaboosted Feature , 2007, Third International Conference on Natural Computation (ICNC 2007).

[15]  Bao-Liang Lu,et al.  Multi-View Gender Classification Using Multi-Resolution Local Binary Patterns and Support Vector Machines , 2007, Int. J. Neural Syst..

[16]  H. Ai,et al.  LUT-Based Adaboost for Gender Classification , 2003, AVBPA.

[17]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[18]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[19]  William T. Freeman,et al.  Understanding belief propagation and its generalizations , 2003 .

[20]  Yun Fu,et al.  Gender recognition from body , 2008, ACM Multimedia.

[21]  Huchuan Lu,et al.  A New Automatic Recognition System of Gender, Age and Ethnicity , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[22]  Bo Wu,et al.  Facial image retrieval based on demographic classification , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[23]  Hui-Huang Hsu,et al.  Fast gender recognition by using a shared-integral-image approach , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  Bo Wu,et al.  Real time facial expression recognition with AdaBoost , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[25]  Huchuan Lu,et al.  Automatic gender recognition based on pixel-pattern-based texture feature , 2008, Journal of Real-Time Image Processing.