INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.7, No.7, Jun. 2015

A Gender Recognition Approach with an Embedded Preprocessing

Full Text (PDF, 607KB), PP.19-27


Views:86   Downloads:2

Author(s)

Md. Mostafijur Rahman, Shanto Rahman, Emon Kumar Dey, Mohammad Shoyaib

Index Terms

Contrast Enhancement, Gender Recognition, Feature Extraction, Classification, Preprocessing

Abstract

Gender recognition from facial images has become an empirical aspect in present world. It is one of the main problems of computer vision and researches have been conducting on it. Though several techniques have been proposed, most of the techniques focused on facial images in controlled situation. But the problem arises when the classification is performed in uncontrolled conditions like high rate of noise, lack of illumination, etc. To overcome these problems, we propose a new gender recognition framework which first preprocess and enhances the input images using Adaptive Gama Correction with Weighting Distribution. We used Labeled Faces in the Wild (LFW) database for our experimental purpose which contains real life images of uncontrolled condition. For measuring the performance of our proposed method, we have used confusion matrix, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR). In every case, our proposed framework performs superior over other existing state-of-the-art techniques.

Cite This Paper

Md. Mostafijur Rahman, Shanto Rahman, Emon Kumar Dey, Mohammad Shoyaib,"A Gender Recognition Approach with an Embedded Preprocessing", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.7, pp.19-27, 2015. DOI: 10.5815/ijitcs.2015.07.03

Reference

[1]R. C. Gonzalez and R. E. Woods, Digital Image Processing. 3rd Edition, Prentice-Hall, Inc, Upper Saddle River, NJ, USA, 2006.

[2]Shih-Chia Huang, Fan-Chieh Cheng, and Yi-Sheng Chiu, “Efficient contrast enhancement using adaptive gamma correction with weighting distribution,” In: IEEE Transaction on image processing, 2013, pp. 1032-1041.

[3]Golomb, B.A., Lawrence, D.T., Sejnowski, T.J., “Sexnet: a neural network identifies sex from human faces,” In: Adv. Neural Inform. Process. Systems (NIPS), 1991, pp. 572–577.

[4]Brunelli, R., Poggio, T., “Hyperbf networks for gender classification,” In: DRAPA Image Understanding Workshop, 1992, pp. 311–314. 

[5]Yang, Z., Li, M., Ai, H., “An experimental study on automatic face gender classification,” In: International Conference on Pattern Recognition (ICPR), 2006, pp. 1099–1102.

[6]Moghaddam, B., Yang, M., “Learning gender with support faces,” In: IEEE Trans. Pattern Anal. Machine Intell, 2002, pp. 707–711.

[7]BenAbdelkader, C., Griffin, P., “A local region-based approach to gender classification from face images,” In: IEEE Conf. on Computer Vision and Pattern Recognition Workshop, 2005, pp. 52–52.

[8]Lapedriza, A., Marin-Jimenez, M.J., Vitria, J., “Gender recognition in non-controlled environments,” In: Internat. Conf. on Pattern Recognition (ICPR), 2006, pp. 834–837.

[9]Dey, E.K.; Muctadir, H.M., "Chest X-ray analysis to detect mass tissue in lung," Informatics, Electronics & Vision (ICIEV), 2014 International Conference on, vol. 1, no. 5, 2014, pp. 23-24.

[10]Baluja, S., Rowley, H.A., “Boosting sex identification performance,” In: Internat. J. Computer Vision, 2007, pp. 111–119.

[11]Mäkinen, E., Raisamo, R., “Evaluation of gender classification methods with automatically detected and aligned faces,” In: IEEE Trans. Pattern Anal. Machine Intell.2008, pp. 541–547.

[12]Hadid, A., Pietikäinen, M., “Combining appearance and motion for face and gender recognition from videos,” In: Pattern Recognition, 2009, pp. 2818–2827.

[13]Shakhnarovich, G., Viola, P.A., Moghaddam, B., “A unified learning framework for real time face detection and classification,” In: IEEE Internat. Conf. on Automatic Face & Gesture Recognition (FG), 2002, pp. 14–21.

[14]Caifeng Shan, “Learning local binary patterns for gender classification on real-world face images,” In: Pattern Recognition Letter, 2012, pp. 431–437.

[15]Dey, Emon Kumar, Mohsin Khan, and Md Haider Ali. "Computer Vision-Based Gender Detection from Facial Image." International Journal of Advanced Computer Science 3, no. 8 (2013).

[16]Powers, D.M.W., "Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation," In: Journal of Machine Learning Technologies, 2011, pp. 37-63.

[17]Huang, G., Ramesh, M., Berg, T., Learned-Miller, E., “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” In: Tech. Rep, University of Massachusetts, Amherst, 2007.

[18]Ahonen, T., Hadid, A., Pietikäinen, M., “Face recognition with local binary patterns”, In: European Conf. on Computer Vision (ECCV), 2004, pp. 469–481.

[19]Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L., “Gender classification based on boosting local binary pattern,” In: Internat. Symp. on Neural Networks, 2006, pp. 194-201.

[20]Lian, H., Lu, B., “Multi-view gender classification using multi-resolution local binary patterns and support vector machines,” In: Internat. J. Neural Systems, 2007, pp. 479–487.

[21]Coltuc, P. Bolon, and J. M. Chassery, “Exact histogram specification,” In: IEEE Transactions on Image Processing, 2006, pp. 1143–1152.

[22]Ojala, T., Pietikäinen, M., Mäenpää, T., “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” In: IEEE Trans. Pattern Anal. Machine Intell.2002, pp. 971–987.

[23]K. Tieu and P. Viola, “Boosting image retrieval,” In: Proc. of Computer Vision and Pattern Recognition, 2000, pp. 228-235.

[24]Jun, Bongjin, and Daijin Kim, "Robust face detection using local gradient patterns and evidence accumulation," In: Pattern Recognition, 2012, pp. 3304-3316.

[25]X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” In: IEEE Transactions on Image Processing, 2010, pp.1635–1650.

[26]Y.-T Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” In: IEEE Transactions on Consumer Electronics, 1997, pp.1-8.

[27]Mary Kim and Min Gyo Chung, “Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement,” In: IEEE Transactions on Consumer Electronics, 2008, pp. 1389 – 1397.

[28]Tin, Hlaing Htake Khaung, "Perceived Gender Classification from Face Images," International Journal of Modern Education and Computer Science (IJMECS), vol. 4, no. 1, pp. 12-18, 2012.

[29]Arai, Kohei, and Rosa Andrie Asmara. "Gender Classification Method Based on Gait Energy Motion Derived from Silhouette Through Wavelet Analysis of Human Gait Moving Pictures," International Journal of Information Technology & Computer Science (IJITCS), vol. 6, no. 3, 2014.

[30]Rahman, Shanto, Md Mostafijur Rahman, Khalid Hussain, Shah Mostafa Khaled, and Mohammad Shoyaib. "Image Enhancement in Spatial Domain: A Comprehensive Study." International Conference on Computer and Information Technology (ICCIT), 2014.

[31]Khalid Hossain, Shanto Rahman, Shah Mostofa Khaled, M. Abdullah Al-Wadud, Dr. Mohammad Shoyaib, "Dark Image Enhancement by Locally Transformed Histogram," 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 2014.