Gender Classification Method Based on Gait Energy Motion Derived from Silhouette Through Wavelet Analysis of Human Gait Moving Pictures

Gender classification method based on Gait Energy Motion: GEM derived through wavelet analysis of human gait moving pictures is proposed. Through experiments with human gait moving pictures, it is found that the extracted features of wavelet coefficients using silhouettes images are useful for improvement of gender classification accuracy. Also, it is found that the proposed gender classification method shows the best classification performance, 97.63% of correct classification ratio.

[1]  Qiang Wu,et al.  Multi-view Gait Recognition Based on Motion Regression Using Multilayer Perceptron , 2010, 2010 20th International Conference on Pattern Recognition.

[2]  Xuelong Li,et al.  Gait Components and Their Application to Gender Recognition , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[4]  Shih-Hsu Chang,et al.  A Lossless Data Hiding based on Discrete Haar Wavelet Transform , 2010, 2010 10th IEEE International Conference on Computer and Information Technology.

[5]  Matjaz Gams,et al.  Medically Driven Data Mining Application: Recognition of Health Problems from Gait Patterns of Elderly , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[6]  Kenichi Takahashi,et al.  Robustness Evaluation of Digital Watermarking Based on Discrete Wavelet Transform , 2010, 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[7]  Koichi Niijima,et al.  DESIGN OF BIORTHOGONAL WAVELET FILTERS USING DYADIC LIFTING SCHEME , 2005 .

[8]  Saeid Nahavandi,et al.  A Review of Vision-Based Gait Recognition Methods for Human Identification , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[9]  W. T. Dempster,et al.  Properties of body segments based on size and weight , 1967 .

[10]  Stephen Lin,et al.  Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval , 2007, IEEE Transactions on Image Processing.

[11]  M. Hanmandlu,et al.  An Experimental Study of Different Features for Face Recognition , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[12]  B L Gunjal,et al.  Discrete Wavelet Transform Based Strongly Robust Watermarking Scheme for Information Hiding in Digital Images , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[13]  Shusaku Nomura,et al.  Determination of Age and Gender Based on Features of Human Motion Using AdaBoost Algorithms , 2011, Int. J. Soc. Robotics.

[14]  Kohei Arai,et al.  Human Gait Gender Classification in Spatial and Temporal Reasoning , 2012 .

[15]  G. Sainarayanan,et al.  Palmprint Based Biometric System: A Comparative Study on Discrete Cosine Transform Energy, Wavelet Transform Energy and SobelCode Methods , 2009 .

[16]  B. Bakshi.  Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[17]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[18]  Mark S. Nixon,et al.  Automatic Recognition by Gait , 2006, Proceedings of the IEEE.

[19]  Qinghan Xiao,et al.  Technology review - Biometrics-Technology, Application, Challenge, and Computational Intelligence Solutions , 2007, IEEE Computational Intelligence Magazine.

[20]  I. Daubechies.  Ten Lectures on Wavelets , 1992 .

[21]  M. Nixon,et al.  Automated Human Recognition by Gait using Neural Network , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

[22]  Youngjoon Han,et al.  Adaptive Gait Pattern Generation of Biped Robot based on Human's Gait Pattern Analysis , 2007 .

[23]  Tieniu Tan,et al.  A Study on Gait-Based Gender Classification , 2009, IEEE Transactions on Image Processing.

[24]  Yiding Wang,et al.  Combining Spatial and Temporal Information for Gait Based Gender Classification , 2010, 2010 20th International Conference on Pattern Recognition.

[25]  W. Eric L. Grimson,et al.  Gait analysis for recognition and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[26]  Yunhong Wang,et al.  Gender Classification Based on Fusion of Multi-view Gait Sequences , 2007, ACCV.

[27]  D. Hatzinakos,et al.  Gait recognition: a challenging signal processing technology for biometric identification , 2005, IEEE Signal Processing Magazine.