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Single Image Face Recognition Using Laplacian of Gaussian and Discrete Cosine Transforms

2010
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Muhammad Sharif , Sajjad Mohsin, Muhammad Younas Javed, Muhammad Atif Ali, IAJIT
Dibuat : 2012-06-23, dengan 1 file

Keyword : Single image, face, recognition, DCT, LOG, and mid frequency values
Subjek : Single Image Face Recognition Using Laplacian of Gaussian and Discrete Cosine Transforms
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.6/3778-9.pdf
Sumber pengambilan dokumen : Internet

This paper presents a single image face recognition approach called Laplacian of Gaussian (LOG) and Discrete


Cosine Transform (DCT). The proposed concept highlights a major concerned area of face recognition i.e., single image per


person problem where the availability of images is limited to one at training side. To address the problem, the paper makes use


of filtration and transforms property of LOG and DCT to recognize faces. As opposed to conventional methods, the proposed


idea works at pre-processing stage by filtering images up to four levels and then using the filtered image as an input to DCT


for feature extraction using mid frequency values of image. Then, covariance matrix is computed from mean of DCT and


Principal component analysis is performed. Finally, distinct feature vector of each image is computed using top Eigenvectors


in conjunction with two LOG and DCT images. The experimental comparison for LOG (DCT) was conducted on different


standard data sets like ORL, Yale, PIE and MSRA which shows that the proposed technique provides better recognition


accuracy than the previous conventional methods of single image per person i.e., (PC)


2


A and PCA, 2DPCA, B-2DPCA etc.


Hence with over 97% recognition accuracy, the paper contributes a new enriched feature extraction method at pre-processing


stage to address the facial system limitations.

Deskripsi Alternatif :

This paper presents a single image face recognition approach called Laplacian of Gaussian (LOG) and Discrete


Cosine Transform (DCT). The proposed concept highlights a major concerned area of face recognition i.e., single image per


person problem where the availability of images is limited to one at training side. To address the problem, the paper makes use


of filtration and transforms property of LOG and DCT to recognize faces. As opposed to conventional methods, the proposed


idea works at pre-processing stage by filtering images up to four levels and then using the filtered image as an input to DCT


for feature extraction using mid frequency values of image. Then, covariance matrix is computed from mean of DCT and


Principal component analysis is performed. Finally, distinct feature vector of each image is computed using top Eigenvectors


in conjunction with two LOG and DCT images. The experimental comparison for LOG (DCT) was conducted on different


standard data sets like ORL, Yale, PIE and MSRA which shows that the proposed technique provides better recognition


accuracy than the previous conventional methods of single image per person i.e., (PC)


2


A and PCA, 2DPCA, B-2DPCA etc.


Hence with over 97% recognition accuracy, the paper contributes a new enriched feature extraction method at pre-processing


stage to address the facial system limitations.

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