Damodara Krishna Kishore Galla,BabuReddyMukkamalla,



Object classification,fuzzy c-means clustering,Eigenvalues,shape,corner,wavelet transform,face recognition ,principal component analysis,


Face analysis is a requisite notion for dissimilar appeal allied to artificial intelligence has made possible for Classification of Gender. Facial Data images are still an arduous task for biometric systems due to diverse expressions, dimensions, pose, illustrations and age in facial and other affiliated images includes dissimilar object label classifications. In this paper, SIFT Probabilistic Fuzzy C-means Clustering Approach (SPFCA) proposed to intensify the stratification methodology in object classification for dissimilar images using GSVM. This approach extremely used for recognition and classification of an object due to its fundamental properties which make decorous contrasting object classification in divergent types of robust in facial and other related images. SPFCA is robust clustering approach to diminish uproar insensitivity and assists to group the vicinity ages, male, female and objects. It also assists to find a solution for coinciding cluster complications which may face preceding clustering approaches. Consequently the proficiency can also be used to increase the comprehensive robustness of face recognition and multi-label object classification system and the result increases its invariance and make it a reliably passable biometric.


I. Chang Huang ,Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, “CNN-RNN: A Unified Framework for Multi-label Image Classification”, Neural Networks and Learning Systems, IEEE Transactions on,2014.
II. Chih-Chin Lai, Chih-Hung Wu1, Shing-Tai Pan, Shie-Jue Lee, and Bor-Haur Lin, “Gender Recognition Using Local Block Difference Pattern”, Advances in Intelligent Information Hiding and Multimedia Signal Processing, page:45-54,Springer International Publishing AG 2017.
III. Chunyu Zhang , Hui Ding ,Yuanyuan Shang , Gender Classification Based on Multiscale Facial Fusion Feature,Hindawi Mathematical Problems in Engineering Volume 2018, ISSN: 1563-5147.
IV. Dai D Q, Yuen P C. Wavelet-based discriminant analysis for face recognition. Applied Mathematics and Computation, 175,1(2006):307-318.
V. Dogucan Yaman1,FevziyeIremEyiokur,NurdanSezgin,”Age and Gender Classification from Ear Images”ACCEPTED FOR IAPR/IEEE IWBF 2018.
VI. D. Ramanan ,T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, P. Doll´ar, and C. L. Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014, pages 740–755. Springer, 2014
VII. G. Ding ,Z. Lin, M. Hu, Y. Lin and S. S. Ge. Image tag completion via dual-view linear sparse reconstructions. Computer Vision and Image Understanding, 124:42–60, 2014.
VIII. G. D. K. Kishore, M.Babu Reddy, Analysis and Prototype Sequences of Face Recognition Techniques in Real-Time Picture Processing, Intelligent Engineering Informatics, Advances in Intelligent Systems and Computing 695, Springer Nature Singapore Pte Ltd-2018.pp.323-335,2018.
IX. G. D. K. Kishore, M.Babu Reddy,” Gender classification based on similarity features through SURF and SVM”,Int. J. Knowledge Engineering and Data Mining, pg No:89-104, Vol. 6, No. 1, 2019.
X. G.D.K. Kishore, M.Babu Reddy, “ Detecting Human and classification of Gender using Facial Images MSIFT Features Based GSVM”, International Journal of Recent Technology and Engineering(IJRTE), pg No:1466-1471, Vol.8, Issue3, Sep2019.
XI. I.Daubechies, Ten lectures on wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 61, SIAM Press, Philadelphia, 1992.
XII. J.Bezdek, “A convergence theorem for the fuzzy ISODATA clustering algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, no.1, pp. 1–8, Jan. 1981
XIII. J.Kittler, M. Hatef, R.P. Duin, J.G. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3) (1998) 226–239.
XIV. J.Mao, W. Xu, Y. Yang, J. Wang, and A. Yuille. Deep captioning with multimodal recurrent neural networks (m-rnn). In ICLR, 2015.
XV. J.Sullivan ,A. S. Razavian, H. Azizpour, and S. Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pages 512–519.IEEE, 2014.
XVI. K.W. Wong, K. M. Lam, and W. C. Siu, “An efficient algorithm for face detection and facial feature extraction under different conditions,” Pattern Recognit., vol. 34, no. 10, pp. 1993–2004, 2001.
XVII. LI Xiao-Fei, “Wavelet Transform for Face Recognition Based on Improved Fuzzy C-Means”, Applied Mechanics and Materials Vols. 602-605 (2014) pp 2170-2173.
XVIII. Mingyuan Xin, Yong Wang “Research on image classification model based on deep convolution neural network”, EURASIP Journal on Image and Video Processing, Springer, page no:2-11,2019.
XIX. MinjSalen Kujur1, Prof. Prashant Jain, “Performance Parameter Analysis of Face Recognition Based On Fuzzy C-Means Clustering, SIFTDetection”, MinjSalenKujur et al. Int. Journal of Engineering Research and Applications” ISSN: 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.515-520.
XX. P.J. Phillips, H. Moon, S.A. Rizvi, P.J. Rauss, The FERET evaluation methodology for face-recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (10) (2000) 1090–1104.
XXI. PuQ,Gao C B, Zhou J L. Theory of fractional covariance matrix and its applications in PCA and 2D-PCA. Expert systems with applications, 40(2013):5395-5401.
XXII. Sandeep Kumar,Sukhwinder Singh,Jagdish Kumar,”A Study on Face Recognition Techniques with Age and Gender Classification” IEEE conference, at Greater Noida, May 2017.
XXIII. S J Raudys, A K Jain. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell. 13 (1991):252–264.
XXIV. T. Sim, S. Baker, M. Bsat, TheCMUpose, illumination, and expression (PIE) database, Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, May 2002.
XXV. W J Krzanowski, P Jonathan, W V McCarthy, M R Thomas. iscriminant analysis with singular covariance matrices: methods and applications to spectroscopic data, Appl. Statist. 44 (1995) :101–115.
XXVI. W. Pedrycz, “Conditional fuzzy clustering in the design of radial basis function neural networks,” IEEE Trans. Neural Netw., vol. 9, no. 4, pp.601–612, Jul. 1998.
XXVII. Yang, J, Zhang D. Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26,1( 2004):131–137.

View Download