GDL

Journal from gdlhub / 2021-08-24 11:54:52
Oleh : Suparna Biswas, Jaya Sil, King Saud University
Dibuat : 2021-08-04, dengan 0 file

Keyword : Contourlet transform, Curvelet transform, Classification, Face recognition, Dimensionality reduction
Url : http://www.sciencedirect.com/science/article/pii/S1319157817300861
Sumber pengambilan dokumen : Web

In the paper we propose a novel method for face recognition using contourlet transform (CNT) and curvelet transform (CLT) which improves rate of face recognition under different challenges. We obtain smooth contour information along different directions by applying CNT on the face image while CLT having multiscale, multidirectional and anisotropic properties has been employed to represent the edges more prominently. Pre-processed training images are decomposed up to fourth level using CNT and coefficients of directional subbands are analysed to obtain the features from the images. In another approach CLT has been applied on the pre-processed face images and considering scale of four and angle eight, different statistical features are extracted from the detail subbands. Finally, we integrate the features obtained from two approaches. High dimensionality of feature space has been reduced by selecting important features depending on the entropy of the transform coefficients. Selected features are applied to recognize the face images using support vector machine (SVM) classifier. Experimental results show that the proposed feature extraction method improves recognition accuracy compare to other methods and efficiently handle the effect of Gaussian noise as tested on JAFFE, ORL and FERET database.

Deskripsi Alternatif :

In the paper we propose a novel method for face recognition using contourlet transform (CNT) and curvelet transform (CLT) which improves rate of face recognition under different challenges. We obtain smooth contour information along different directions by applying CNT on the face image while CLT having multiscale, multidirectional and anisotropic properties has been employed to represent the edges more prominently. Pre-processed training images are decomposed up to fourth level using CNT and coefficients of directional subbands are analysed to obtain the features from the images. In another approach CLT has been applied on the pre-processed face images and considering scale of four and angle eight, different statistical features are extracted from the detail subbands. Finally, we integrate the features obtained from two approaches. High dimensionality of feature space has been reduced by selecting important features depending on the entropy of the transform coefficients. Selected features are applied to recognize the face images using support vector machine (SVM) classifier. Experimental results show that the proposed feature extraction method improves recognition accuracy compare to other methods and efficiently handle the effect of Gaussian noise as tested on JAFFE, ORL and FERET database.

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