A Study on Gender and Age Classification as the Two Most Vital Tools in the Identification and Verification System

Abstract

Gender identification and age classification is one of the challenging aspect in biometric authentication and verification system which capture walk from far distance and study physical information of the subject such as gender, race and emotional state of the subject. It was established that most of the gender identification methods have focused only with frontal pose of diverse human subject, image size and type of database used in the procedure. Different feature extraction process such as, Principal Component Analysis (PCA) and Local Directional Pattern (LDP) that are used to extract the authentication features of a person will also be classified in this study. The aims of this paper, is to analyze the different gender classification methods and age estimation framework in computer vision that help in evaluating strength and weakness of existing gender identification algorithm. Hence, a new gender classification algorithm will be develop with less computational cost and accuracy. An overview as well as classification of various gender identification methods will be presented first and then compared with other existing human identification system by means of their performance.

Keywords

• Aging pattern,Feature selection,Feature extraction,Human identification system,Gender classification methods

References

1. Mathivanan, P., & Poornima, K. (2018, February). Series B 99(1):79–85. Biometric Authentication for Gender Classification Techniques: A Review. Journal of the Institution of Engineers (India): https://www.researchgate.net/publication/321637452 Makinen, E., & Raisamo, R. (2008). Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 Tapia, J.E., & Perez, C.A. (2013). Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape. IEEE Trans. Inf. Forensics Secur. 8(3), 488–499 S. Gutta, et al., in Comparative performance evaluation of grayscale and color information for face recognition tasks. AVBPA (2001) Wei-Lun Chao n, Jun-Zuo Liu and Jian-Jiun Ding, Vol 46, Issue 3, March 2013. Facial age estimation based on label-sensitive learning and age-oriented regression. D. Stewart, A. Pass, J. Zhang, Gender classification via lips: static and dynamic features. IET Biom. 2(1), 28–34 (2013) K. Arai, R. Andrie, Gender classification with human gait based on skeleton model. in 2013 Tenth International Conference on Information Technology: New Generations (ITNG), pp. 113–118 (2013) M. Wu, J. Zhou, J. Sun, Multi-scale ICA texture pattern for gender recognition. Electron. Lett. 48(11), 629–631 (2012) L. Ballihi et al., Boosting 3-D-geometric features for efficient face recognition and gender classification. IEEE Trans. Inf. Forensics Secur. 7(6), 1766–1779 (2012) L. Ballihi, et al., Geometric based 3D facial gender classification. In Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on. IEEE (2012). J. Tang, X. Liu, H. Cheng, K.M. Robinette, Gender recognition using 3-D human body shapes. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 898–908 (2011). Hu, M., Wang, Y., Zhang, Z., & Zhang, D. (2011). Gait based gender classification using mixed conditional random field. Part B Cybern. IEEE Trans. Syst. Man Cybern. 41(5), 1429–1439 Gutta, S., Huang, J, R. J., Jonathon, P., & Wechsler, H. (2000). Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Trans. Neural Netw. 11(4), 948–960 Ke Chen, Shaogang Gong, Tao Xiang and Chen Change Loy, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2467-2474.Cumulative Attribute Space for Age and Crowd Density Estimation. Kaur, K.D., & Rai, P. (2017, August). An Analysis on Gender Classification and Age Estimation Approaches. International Journal of Computer Applications (0975 – 8887) Volume 171 – No. 10. Bekios, C.J., Buenaposada, J.M., & Baumela, L. (2011). Revisiting linear discriminant techniques in gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 858–864 Bianco, S., Gasparini, F., & Schettini, R. (2015). Adaptive skin classification using face and body detection. IEEE Trans. Image Process. 24(12), 4756–4765 Hu Han, Charles Otto, and Anil K. Jain, Vol. 37, No. 6, pp. 1148-1161, June 2015. Age Estimation from Face Images: Human vs. Machine Performance. Hu Han, Charles Otto, Xiaoming Liu and Anil K. Jain, IEEE transactions on Pattern analysis and Machine intelligence, Vol 37, no. 6, pp.1148-1161, October 2014. Demographic Estimation from Face Images:Human vs. Machine Performance. Juan Bekios-Calfa, Jose m. Buenaposada and Luis Baumela, Vol 36, pp 228-234. Pattern Recognition Letters 2013. Robust gender recognition by exploting facial attributes dependencies.