Robust gender classification using a precise patch histogram

This study proposed a precise facial feature extraction method to improve the accuracy of gender classification under pose and illumination variations. We used the active appearance model (AAM) to align the face image. Images were modeled by the patches around the coordinates of certain landmarks. Using the proposed precise patch histogram (PPH) enabled us to improve the accuracy of the global facial features. The system is composed of three phases. In the training phase, non-parametric statistics were used to describe the characteristics of the training images and to construct the patch library. In the inference phase, the choice of feature patch from the library needed to approximate the patch of the testing image was based on the maximum a posteriori estimation. In the estimation phase, a Bayesian framework with portion-oriented posteriori fine-tuning was employed to determine the classification decision. In addition, we developed the dynamic weight adaptation to obtain a more convincing performance. The experimental results demonstrated the robustness of the proposed method. Highlights? This paper presents a precise patch histogram (PPH) to improve the performance. ? A patch-based feature acquisition with AAM algorithm is proposed. ? The system includes a library selection using eigenface and k-means clustering. ? The accuracy of the global facial features was evidently improved by using the PPH. ? A portion-oriented posteriori fine-tuning was used to improve the classification.

[1]  XueMing Leng,et al.  Gender classification based on fuzzy SVM , 2008, 2008 International Conference on Machine Learning and Cybernetics.

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

[3]  Matti Pietikäinen,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON MULTIMEDIA 1 Lipreading with Local Spatiotemporal Descriptors , 2022 .

[4]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jing Li,et al.  A Framework for Multi-view Gender Classification , 2007, ICONIP.

[7]  Bin Xia,et al.  Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[8]  Matthew Toews,et al.  Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Shumeet Baluja,et al.  Boosting Sex Identification Performance , 2005, International Journal of Computer Vision.

[10]  Zhen Li,et al.  Spatial Gaussian Mixture Model for gender recognition , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[13]  Roope Raisamo,et al.  Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  André Gooßen,et al.  Face recognition under pose variations using shape-adapted texture features , 2010, 2010 IEEE International Conference on Image Processing.

[15]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[16]  Matti Pietikäinen,et al.  Combining appearance and motion for face and gender recognition from videos , 2009, Pattern Recognit..

[17]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

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

[20]  Yunus Saatci,et al.  Cascaded classification of gender and facial expression using active appearance models , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[21]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[22]  Peng Li,et al.  Patch-based within-object classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  A. R . Ardakany,et al.  Gender Recognition Based on Edge Histogram , 2012 .

[24]  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.

[25]  Shengcai Liao,et al.  Illumination Invariant Face Recognition Using Near-Infrared Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  M. Melamed.  Detection , 2021, SETI: Astronomy as a Contact Sport.