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Abstract
 Facial age estimation recently becomes active research topic in pattern recognition. As there are vast potential application in age specific human computer interaction security control and surveillance monitoring. Insufficient and incomplete training data, uncontrollable environment, facial expression are the most prominent challenges in facial age estimation. Degree of accuracy for age estimation is obtained by forming appropriate feature vector of a facial image. Feature vectors are constructed from facial features. Therefore comparative study of feature extraction from facial image by bio inspired feature (BIF), histogram of gradient (HOG), Gabor filter, wavelet transform and scattering transform is done. The propose approach exploits scattering transform gives more information about features of the facial images. Well organized system consist scattering transform that disperse gabber coefficients pulling with smooth gaussian process in number of layers which isused to calculate for facial feature representation. These extracted features are classified using support vector machine and artificial neural network.
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References
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References
C. J. Taylor, T. F. Cootes and A. Lanitis, “Toward automatic simulation of aging effects on face images,†IEEE Transaction. on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 442–455, 2002. DOI: https://doi.org/10.1109/34.993553
J. B. Pittenger et al, L. S. Mark, J. T. Todd and R. E. Sha, “The perception of human growth.†Scientific American, volume 242, no. 2, p. 132, 1980. DOI: https://doi.org/10.1038/scientificamerican0280-132
K. Smith-Miles, Z. Zhou X. Geng, “Automatic age estimation based on facial aging patterns,†IEEE Trans. on Pattern Analysis and Machine Intelligence, volume 29, no. 12, pp- 2234–2240, 2007. DOI: https://doi.org/10.1109/TPAMI.2007.70733
H. Dai, Z.-H. Zhou, G. Li X. Geng andY. Zhang, “Learning from facial aging patterns for automatic age estimation,†in Proc. ACM International Conference. on Multimedia, 2006.
W. Gao, X. Chen, J. Suo, S. Shan, “Learning long term face aging patterns from partially dense aging databases,†in Proc. IEEE International Conf. on Computer Vision, 2009.
Z. Yang, H. Ai,“Demographic classification with local binary patterns,†in Advances Biometrics, 2007.
H. Lu, Q. Liu, C. Li, J. Liu, “Learning ordinal discriminative features for age estimation,†in Proc. IEEE Int’l Conference. on Computer Vision and Pattern Recognition, 2012.
B. Ni, Z. Song, S. Yan, “Web image mining towards universal age estimator,†in Proc. ACM International Conference on Multimedia, 2009. DOI: https://doi.org/10.1145/1631272.1631287
T. Poggio, T. Serre and L. Wolf, “Object recognition with features inspired by visual cortex†In Conference on Computer. Vision and Pattern Recognit., 2005.
M. Riesenhuber, T. Poggio, “Hierarchical models of object recognition in cortex,†Nature Neuroscience, 2(11):1019–1025, 1999. DOI: https://doi.org/10.1038/14819
Saeid. Fazli, Leila. Ali Heidarloo, “Wavelet Based Age Invariant Face Recognition using Gradient Orientation,†International Conference on Advances in Computer and Electrical Engineering (ICACEE'2012) November. 17-18, 2012 Manila (Philippines).
Dr. S. Muttan and P. Gnanasivam, “Estimation of Age Through Fingerprints Using WaveletTransform and Singular Value Decomposition,†International Journal of Biometrics and Bioinformatics (IJBB), Vol(6) : Issue (2) : 2012.
Ms.Reeta Rani, Mr.Kuldeep Sharma, Mr.RakeshDhiman , “Human age estimation by gabor& fuzzy k-means,â€2nd international conference on science, technology and management ,university of delhi, conference center, new delhi 27 sept 2015.
Kuangyuchang, Chu song chen, “ A learning framework for age estimation based on face images with scattering transform,†ieee transaction on image processing,2015. DOI: https://doi.org/10.1109/TIP.2014.2387379