Abstract
With the advances in technology, facial recognition has become a very popular technology to be used majorly as a security technique. Face recognition using support vector machine is being used since years but it does not work well with imbalanced data and computational time which is more. In this work, an enhanced support vector machine (ESVM) is utilized for multi-classifying the face images. Fisher space method is utilized for feature extraction as it is more efficient for dataset consisting of multiple classes with class separability as a vital attribute while compressing dimensionality. It concentrates on type of features from face image data that provide a better demarcation for separation of face images, and then, ESVM-based multi-classification is utilized for classification purpose. The advantage of ESVM-based multiclass classification for face images includes flexibility, enhanced computational time. ESVM-based multiclass classification based on One-vs-One (OVO) and One-vs-All (OVA) which is utilized for performing experiment on two standard databases such as Yale and ORL. A number of experiments are also performed varying sub-dimensions in fisher space with different kernels of proposed ESVM on different training sets of both databases. A remarkable recognition rate of 100 and 92.5% was achieved on Yale and ORL database, respectively.
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References
Li, S. Z., & Jain, A. K. (2005). Handbook of face recognition. Springer.
Okokpujie, K., Noma-Osaghae, E., John, S., Grace, K., & Okokpujie, I. (2017). A face recognition attendance system with GSM notification. In IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), Owerri (pp. 239–244). https://doi.org/10.1109/NIGERCON.2017.8281895.
Soltanpour, S., Boufama, B., Jonathan Wu, Q. M. (2017). A survey of local feature methods for 3D face recognition. Pattern Recognition, 72, 391–406. ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2017.08.003.
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld. (2003). Face recognition: A literature survey. ACM Computing Surveys, 35(4), 399–458. https://doi.org/10.1145/954339.954342.
Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71–86. https://doi.org/10.1162/jocn.1991.3.1.71
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720. https://doi.org/10.1109/34.598228.
Zhang, Z., Lyons, M., Schuster, M., & Akamatsu, S. (1998). Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron. In Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara (pp. 454–459). https://doi.org/10.1109/AFGR.1998.670990.
Mahmood, M., Jalal, A., Evans, H. A. (2018). Facial expression recognition in image sequences using 1D transform and gabor wavelet transform. In 2018 International Conference on Applied and Engineering Mathematics (ICAEM), Taxila (pp. 1–6).
Kathuria, D., & Yadav, J. (2018). An improved illumination invariant face recognition based on Gabor wavelet transform. In 2018 Conference on Information and Communication Technology (CICT), Jabalpur, India (pp. 1–6). https://doi.org/10.1109/INFOCOMTECH.2018.8722408.
Yadav, J., Rajpal, N., & Vishwakarma, V. P. (2016). Face recognition using Symlet, PCA and cosine angle distance measure. In 2016 Ninth International Conference on Contemporary Computing (IC3), Noida (pp. 1–7). https://doi.org/10.1109/IC3.2016.7880231.
Yin, X., & Liu, X. (2018). Multi-task convolutional neural network for pose-invariant face recognition. IEEE Transactions on Image Processing, 27(2), 964–975. https://doi.org/10.1109/TIP.2017.2765830
Yadav, J., Rajpal, N., & Mehta, R. (2018). A new illumination normalization framework via homomorphic filtering and reflectance ratio in DWT domain for face recognition. Journal of Intelligent and Fuzzy Systems, 35, 5265–5277.
Balasundaram, A., & Ashokkumar, S. (2020). Study of facial expression recognition using machine learning techniques. Journal of Critical Reviews, 7(8), 2429–2437.
Yadav, J., Rajpal, N., & Mehta, R. (2019). An improved illumination normalization and robust feature extraction technique for face recognition under varying illuminations. Arabian Journal for Science and Engineering, 44(11), 9067–9086.
Martis, R. J., Acharyaa, U. R., & Min, L. C. (2013). ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 8, 437–448. https://doi.org/10.1016/j.bspc.2013.01.005
Bajrami, X., Gashi, B., & Murturi, I. (2018). Face recognition performance using linear discriminant analysis and deep neural networks. International Journal of Applied Pattern Recognition, 5(3), 240–250.
Yadav, J., Rajpal, N., & Mehta, R. (2018). An improved hybrid illumination normalization and feature extraction model for face recognition. International Journal of Applied Pattern Recognition, 5(2), 149–170. https://doi.org/10.1504/IJAPR.2018.092523
Soman, K. P., Loganathan, R., & Ajay, V. (2009). Machine learning with SVM and other Kernel methods. PHI Learning.
Rajpal, N., Singh, A., & Yadav, J. (2018). An expression invariant face recognition based on proximal support vector machine. In 2018 4th International Conference for Convergence in Technology (I2CT) (pp. 1–7). https://doi.org/10.1109/I2CT42659.2018.9058243.
Ouyanga, A., Liub, Y., Pei, S., Penga, X., He, M., & Wang, Q. (2020) A hybrid improved kernel LDA and PNN algorithm for efficient face recognition. Neurocomputing, 393, 214–222. https://doi.org/10.1016/j.neucom.2019.01.117.
Zhang, T., Tang, Y. Y., Fang, B., Shang, Z., & Liu, X. (2009). Face recognition under varying illumination using gradientfaces. IEEE Transactions on Image Processing, 18(11), 2599–2606. https://doi.org/10.1109/TIP.2009.2028255
Nayef Al-Dabagh, M. Z., Mohammed Alhabib, M. H., & AL-Mukhtar, F. H. (2018). Face recognition system based on kernel discriminant analysis, k-nearest neighbor and support vector machine. International Journal of Research and Engineering, 5(3), 335–338.
Rakshit, P., Basu, R., Paul, S., Bhattacharyya, S., Mistri, J., & Nath, I. (2019). Face detection using support vector machine with PCA. In 2nd International Conference on Non-Conventional Energy: Nanotechnology and Nanomaterials for Energy and Environment (ICNNEE).
Gumus, E., Kilic, N., Sertbas, A., & Ucan, O. N. (2010). Evaluation of face recognition using PCA, wavelets and SVM. Expert Systems with Applications, 37, 6404–6408. https://doi.org/10.1016/j.eswa.2010.02.079.
Abikoye, O. C., Shoyemi, I. F., & Aro, T. O. (2019). Comparative analysis of illumination normalizations on principal component analysis based feature extraction for face recognition. FUOYE Journal of Engineering and Technology, 4(1), 67–69.
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Jain, T., Yadav, J. (2022). An Enhanced Support Vector Machine for Face Recognition in Fisher Subspace. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_32
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DOI: https://doi.org/10.1007/978-981-16-3346-1_32
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