Face Recognition System Based on Gabor Wavelets Transform, Principal Component Analysis and Support Vector Machine
Face Recognition is a well-known image analysis application in the branches of pattern recognition and computer vision. It utilizes the uniqueness of human facial characteristics for personnel identification and verification. For a long time, the recognition of facial expressions by using computer-based applications has been an active area of study to recognize face scheme through a face image database. It is used in a variety of essential fields of modern life such as security systems, criminal identification, video retrieval, passport and credit cards. In general, face recognition process can be summarized in three distinct steps: preprocessing, feature extraction, and classification. At first, histogram equalization and median filter are applied as preprocessing methods. Secondly, Gabor wavelets transform extracts the features of desirable facial characterized by, orientation selectivity, spatial locality, and spatial frequency to keep up the variations caused by the varying of facial expression and illumination. In addition to that, Principal Component Analysis methodology (PCA) is used in dimensionality reduction. At last, Support vector machine (SVM) is applied in classifying the feature of the image according to the classis of every mage. In order to test the approach used in this research, experiments were running on Yale database of 165 images from 15 individuals in MATLAB environment. The results obtained from the experiments confirmed the accuracy and robustness of the proposed system.
Yukti Bakhshi, Sukhvir Kaur, and Prince Verma, "An Efficient Approach in Face Recognition for Invariant Faces using SIFT, SURF, and PCA," International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.9, No.5, pp. 99-108, 2016.
S. Sharma, “Face Recognition using PCA and SVM with Surf Technique,” International Journal of Computer Applications, vol. 129, no. 4, pp. 41–46, 2015.
W. Wang, X. Sun, S. Karungaru, and K. Terada, "Face Recognition Algorithm Using Wavelet Decomposition and Support Vector Machines," IEEE International Symposium on Optomechatronic Technologies (ISOT), pp. 1-6, Oct. 2012.
Alpa Choudhary and Rekha Vig, "Face recognition using multiresolution wavelet combining discrete cosine transform and Walsh transform," Proceedings of the 2017 International Conference on Biometrics Engineering and Application, 2017, pp.33-38,
Priyanka, Dr. Yashpal Singh, “A Study on Facial Feature Extraction and Facial Recognition Approaches,” International Journal of Computer Science and Mobile Computing, vol. 4, pp. 166-174, 2014.
D. Gabor, “Theory of communication,” J. Inst. Elect. Eng., vol. 93, no. 26, pt. III, pp. 429–457, 1946.
S. Linlin, and L.Bai. "A review on Gabor wavelets for face recognition." Pattern analysis and applications, vol. 9, no. 2-3, pp. 273-292, 2006.
S. Tang, "Face recognition method based on Gabor wavelet and memetic ecological algorithm," Biomedical Research, vol. 29, no. 0, pp. 1-1, 2017.
A. Vinay, V. S. Shekhar, K. N. B. Murthy, and S. Natarajan, "Face Recognition Using Gabor Wavelet Features with PCA and KPCA - A Comparative Study," Procedia Computer Science, vol. 57, pp. 650-659, 2015.
B.S. Oh, K.A. Toh, A. Teoh, and Z. Lin., "An analytic Gabor feedforward network for single-sample and pose-invariant face recognition." IEEE Trans. Image Process. Vol. 27 no. 6, pp. 2791–2805, 2018.
L. A. Cament, F. J. Galdames, K. W. Bowyer, and C. A. Perez, "Face recognition under pose variation with local Gabor features enhanced by active shape and statistical models," Pattern Recognition, Vol. 48, no. 11, pp. 3371-84, Nov. 2015.
M.Turk and A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive Neuroscience, Vol. 3, pp. 71-. 86, 1991
C. Liu and H. Wechsler, “Independent component analysis of Gabor features for face recognition,” IEEE Trans. Neural Networks, vol. 14, no. 4, pp. 919-928, 2003.
M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification," Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015
Bayezid Islam, Firoz Mahmud, Arafat Hossain, Md. Sumon Mia, Pushpen Bikash Goala, "Human Facial Expression Recognition System Using Artificial Neural Network Classification of Gabor Feature Based Facial Expression Information," IEEE 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), 13-15 September 2018.
Zhifeng Li and Xiaoou Tang, "Bayesian face recognition using support vector machine and face clustering," Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, Vol. 2, pp. II-374-II-380.
Mustafa Zuhaer AL-Dabagh, Dr. Firas H. ALMukhtar." Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine", International Journal of Advanced Engineering Research and Science (ISSN: 2349-6495(P) | 2456-1908(O)), vol.4, no. 3, pp.258- 263, 2017.
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