Abstract
This paper proposes an integrated framework to recognize face images using compressive sensing (CS), curvelet transform (CT), and Principal Component Analysis (PCA). Here CS is used to offer the compressive measurements of image which leads to reduced storage space and computational time complexity. Facial images are rich with the lines, the edges, curvatures and the boundaries. CT has been used to represent the face images in compact form playing dual role, (i) sparse representation to offer compressive measurements on detailed subband and (ii) enhancement of face images by reconstruction. PCA is then applied on enhanced images to select important features for recognition. The performance of the proposed method is evaluated by employing K-fold cross validation technique, collaborative representation based classifier with regularized least square (CRC_RLS), neural network (NN), Naive Bayes (NB) and Support Vector Machine (SVM). Extensive experiments on publicly available ORL face database is conducted to substantiate our claim.
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Biswas, S., Sil, J., Maity, S.P. (2017). PCA Based Face Recognition on Curvelet Compressive Measurements. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_18
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DOI: https://doi.org/10.1007/978-981-10-6427-2_18
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