Face Recognition Using Eigen face Coefficients and Principal Component Analysis
Face Recognition is a field of multidimensional
applications. A lot of work has been done, extensively on the most of
details related to face recognition. This idea of face recognition using
PCA is one of them. In this paper the PCA features for Feature
extraction are used and matching is done for the face under
consideration with the test image using Eigen face coefficients. The
crux of the work lies in optimizing Euclidean distance and paving the
way to test the same algorithm using Matlab which is an efficient tool
having powerful user interface along with simplicity in representing
complex images.
[1] Wendy S. Yambor Bruce A. Draper J. Ross Beveridge, "Analyzing PCA
based Face Recognition Algorithms: Eigenvector Selection and Distance
Measures", July 1, 2000. Available at:
http://www.cs.colostate.edu/~vision/publications/eemcvcsu2000.pdf
[2] Peter Belhumeur, J. Hespanha, David Kriegman, "Eigenfaces vs.
fisherfaces: Recognition using class specific linear projection", IEEE
Transactions on Pattern Analysis and Machine Intelligence, 19(7):771 -
720, 1997.
[3] L. Breiman. Bagging predictors. Technical Report Technical Report
Number 421, Dept. of Statistics, University of California, Berkeley,
1994.
[4] D. Swets and J. Weng, "Hierarchical discriminant analysis for image
retrieval", IEEE Transactions on Pattern Analysis and Machine
Intelligence, 21(5):386-401, 1999.
[5] Wendy S. Yambor, "Analysis of PCA Based and Fisher Discriminant-
Based Image Recognition Algorithms", M.S. Thesis, July 2000
(Technical Report CS-00-103, Computer Science).
[6] Kyungnam Kim, "Face Recognition using Principle Component
Analysis",. International Conference on Computer Vision and Pattern
Recognition, pp. 586-591, 1996.
[7] http://scien.stanford.edu/class/ee368/projects2001/dropbox/project16/
[8] http://www.irc.atr.jp/%7Emlyons/pub_pdf/fg98-1.pdf
[9] http://www.kasrl.org/jaffe.html
[10] James R. Parker, J R Parker , "Algorithms for Image Processing and
Computer Vision", John Wiley & Sons, 1996.
[11] Sankar K. Pal, Ashish Ghosh, Malay K. Kundu, "Soft Computing for
Image Processing", Studies in Fuzziness and Soft Computing, Vol. 42,
2000.
[12] Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing",
Pearson Publications, 2000.
[13] Image Processing Handbook by John C. Russ
[14] Handbook of Pattern Recognition and Image Processing by K.S. Fu and
T.Y. Young
[15] Li Ma , Tieniu Tan , Yunhong Wang , Dexin Zhang " Personal
Identification Based on Iris Texture Analysis" , IEEE Transactions on
Pattern Analysis and Machine Intelligence , Vol. 25 No. 12, December
2003.
[16] John Carter, Mark Nixon, "An Integrated Biometric Database",
available at: ieeexplore.ieee.org/iel3/1853/4826/00190224.pdf.
[17] Arun Rose, Anil Jain and Sharat Pankanti, "A Prototype Hand
Geometry Based Verification System", 2nd International Conference on
Audio and Video Based Person Authentication, Washington D. C., pp.
166-171, 1999.
[18] Boreki, Guilherm, Zimmer, Alessandro, "Hand Geometry Feature
Extraction through Curvature Profile Analysis", XVIII Brazilian
Symposium on Computer Graphics and Image Processing, SIBGRAPI,
Brazil, 2005.
[1] Wendy S. Yambor Bruce A. Draper J. Ross Beveridge, "Analyzing PCA
based Face Recognition Algorithms: Eigenvector Selection and Distance
Measures", July 1, 2000. Available at:
http://www.cs.colostate.edu/~vision/publications/eemcvcsu2000.pdf
[2] Peter Belhumeur, J. Hespanha, David Kriegman, "Eigenfaces vs.
fisherfaces: Recognition using class specific linear projection", IEEE
Transactions on Pattern Analysis and Machine Intelligence, 19(7):771 -
720, 1997.
[3] L. Breiman. Bagging predictors. Technical Report Technical Report
Number 421, Dept. of Statistics, University of California, Berkeley,
1994.
[4] D. Swets and J. Weng, "Hierarchical discriminant analysis for image
retrieval", IEEE Transactions on Pattern Analysis and Machine
Intelligence, 21(5):386-401, 1999.
[5] Wendy S. Yambor, "Analysis of PCA Based and Fisher Discriminant-
Based Image Recognition Algorithms", M.S. Thesis, July 2000
(Technical Report CS-00-103, Computer Science).
[6] Kyungnam Kim, "Face Recognition using Principle Component
Analysis",. International Conference on Computer Vision and Pattern
Recognition, pp. 586-591, 1996.
[7] http://scien.stanford.edu/class/ee368/projects2001/dropbox/project16/
[8] http://www.irc.atr.jp/%7Emlyons/pub_pdf/fg98-1.pdf
[9] http://www.kasrl.org/jaffe.html
[10] James R. Parker, J R Parker , "Algorithms for Image Processing and
Computer Vision", John Wiley & Sons, 1996.
[11] Sankar K. Pal, Ashish Ghosh, Malay K. Kundu, "Soft Computing for
Image Processing", Studies in Fuzziness and Soft Computing, Vol. 42,
2000.
[12] Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing",
Pearson Publications, 2000.
[13] Image Processing Handbook by John C. Russ
[14] Handbook of Pattern Recognition and Image Processing by K.S. Fu and
T.Y. Young
[15] Li Ma , Tieniu Tan , Yunhong Wang , Dexin Zhang " Personal
Identification Based on Iris Texture Analysis" , IEEE Transactions on
Pattern Analysis and Machine Intelligence , Vol. 25 No. 12, December
2003.
[16] John Carter, Mark Nixon, "An Integrated Biometric Database",
available at: ieeexplore.ieee.org/iel3/1853/4826/00190224.pdf.
[17] Arun Rose, Anil Jain and Sharat Pankanti, "A Prototype Hand
Geometry Based Verification System", 2nd International Conference on
Audio and Video Based Person Authentication, Washington D. C., pp.
166-171, 1999.
[18] Boreki, Guilherm, Zimmer, Alessandro, "Hand Geometry Feature
Extraction through Curvature Profile Analysis", XVIII Brazilian
Symposium on Computer Graphics and Image Processing, SIBGRAPI,
Brazil, 2005.
@article{"International Journal of Information, Control and Computer Sciences:52405", author = "Parvinder S. Sandhu and Iqbaldeep Kaur and Amit Verma and Samriti Jindal and Inderpreet Kaur and Shilpi Kumari", title = "Face Recognition Using Eigen face Coefficients and Principal Component Analysis", abstract = "Face Recognition is a field of multidimensional
applications. A lot of work has been done, extensively on the most of
details related to face recognition. This idea of face recognition using
PCA is one of them. In this paper the PCA features for Feature
extraction are used and matching is done for the face under
consideration with the test image using Eigen face coefficients. The
crux of the work lies in optimizing Euclidean distance and paving the
way to test the same algorithm using Matlab which is an efficient tool
having powerful user interface along with simplicity in representing
complex images.", keywords = "Eigen Face, Multidimensional, Matching, PCA.", volume = "3", number = "4", pages = "973-5", }