A Gender Recognition System from Facial Image

Automatic gender classification from facial image has become an attractive research area in the field of machine learning. Various methods have already been proposed for gender recognition in both controlled and uncontrolled situations. Problem arises in uncontrolled situation when there are high rate of noises, lack of illumination etc. To mitigate the problems, we have proposed a framework where we applied a pre-processing to enhance the images using Bilateral Histogram Equalization (BHEP) algorithm and applied the proposed framework in LFW, Adience and color FERET dataset yielding 94.29%, 84.86% and 98.30% accuracies. Confusion matrix, Precision, Recall, F-measure, True Positive Rate (TPR), True Negative Rate (TNR) etc. also shows that our proposed method performs better than the existing state of the arts.

[1]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[2]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[3]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[4]  WENHAO ZHANG,et al.  Gender recognition from facial images: two or three dimensions? , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[8]  Andrew Zisserman,et al.  Fisher Vector Faces in the Wild , 2013, BMVC.

[9]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Shih-Chia Huang,et al.  Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution , 2013, IEEE Transactions on Image Processing.

[11]  Yeong-Taeg Kim,et al.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[12]  Tal Hassner,et al.  Age and Gender Estimation of Unfiltered Faces , 2014, IEEE Transactions on Information Forensics and Security.

[13]  Caifeng Shan,et al.  Learning local binary patterns for gender classification on real-world face images , 2012, Pattern Recognit. Lett..

[14]  Shanto Rahman,et al.  Bilateral histogram equalization with pre-processing for contrast enhancement , 2016, 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[15]  Bok-Min Goi,et al.  Vision-based Human Gender Recognition: A Survey , 2012, ArXiv.

[16]  Mohammad Shoyaib,et al.  An Automated System for Garment Texture Design Class Identification , 2015, Comput..

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

[18]  Tal Hassner,et al.  Age and gender classification using convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[19]  Terrence J. Sejnowski,et al.  SEXNET: A Neural Network Identifies Sex From Human Faces , 1990, NIPS.

[20]  Xiaoou Tang,et al.  Facial Landmark Detection by Deep Multi-task Learning , 2014, ECCV.

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

[22]  Robert Laganière,et al.  Real-time embedded age and gender classification in unconstrained video , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[23]  KimYeong-Taeg.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[24]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[25]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Qian Chen,et al.  Image enhancement based on equal area dualistic sub-image histogram equalization method , 1999, IEEE Trans. Consumer Electron..

[27]  Paul A. Viola,et al.  A unified learning framework for real time face detection and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[28]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[29]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[30]  Shanto Rahman,et al.  A Gender Recognition Approach with an Embedded Preprocessing , 2015 .

[31]  Oksam Chae,et al.  Local Directional Texture Pattern image descriptor , 2015, Pattern Recognit. Lett..

[32]  Inho Choi,et al.  Local Transform Features and Hybridization for Accurate Face and Human Detection , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  James M. Rehg,et al.  CENTRIST: A Visual Descriptor for Scene Categorization , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  M. Abdullah-Al-Wadud,et al.  Noise adaptive binary pattern for face image analysis , 2015, 2015 18th International Conference on Computer and Information Technology (ICCIT).