Abstract
In this study, we analyze and study the feature extraction method based on wavelet transform and Principal Component Analysis (PCA), and propose a method combining symlet 2 wavelet (sym2) and Support Vector Machine (SVM) for face recognition. Firstly, the sym2 is used to wavelet decomposition of the original image, and the decomposed low-frequency information is selected for image reconstruction. Then, the reconstructed data is multi-classified using support vector machine. Experiments were carried out using the internationally published ORL face recognition database, and compared with some classical methods used in ORL face recognition. The experimental results show that the recognition success rate increases with the increase in the training set. When 7 poses are selected for each face (A total of 280 photos are used as training sets) in the data set, the recognition success rate reaches 100%. Therefore, the method proposed in this paper is better than the previous method.
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This work was supported by Key Research and Development Plan Project of Shandong Province, China (No. 2017CXGC0614).
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Li, Z., Jiang, X., Pang, Y. (2019). Evaluation of Face Recognition Techniques Based on Symlet 2 Wavelet and Support Vector Machine. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11637. Springer, Cham. https://doi.org/10.1007/978-3-030-24900-7_19
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