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Pattern recognition can be defined as the categorization of input data into identifiable classes via the extraction of significant features or attributes of the data from a background of irrelevant detail.
A pattern class is a category determined by some given common attributes or features.
A pattern is the description of any member of a category representing a pattern class.
Supervised pattern recognition is characterized by the fact that the correct classification of every training pattern is known
Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face classification. It is considered the first successful example of facial recognition technology
One of the most important parts of the algorithm is the Line Segment Hausdorff Distance (LHD) described to accomplish an accurate matching of face images The images are encoded into binary edge maps using Sobel edge detection algorithm.
The main strength of this distance measurement is that measuring the parallel distance, we choose the minimum distance between edges
The eigenfaces algorithm is related to the threshold to determine a match in the input image It was demonstrated that the accuracy of recognition could achieve perfect recognition; however, the quantity of image rejected as unknown increases.
The results show that there is not very much changes with lighting variations; whereas size changes make accuracy fall very quickly
LEM algorithm demonstrated a better accuracy than the eigenfaces methods with size variations. While eigenfaces difficultly achieved an acceptable accuracy, LEM manage to obtain percentages around 90%, something very good for a face recognition algorithm.
The results from  for orientation changes, LEM algorithm could not beat eigenfaces method. LEM hardly reach a 70% for all different poses.
LEM is based on face features, while eigenfaces uses correlation and eigenvector to do so.
Both the algorithms has different approach to recognize a face the merit of one algorithms is the drawback of another one but the combination of these two algorithms results in providing maximum accuracy in face recognition .
 De Vel, O.; Aeberhard, S., “Line-based face recognition under varying pose” .Pattern Analysis and Machine Intelligence, IEEE Transactions on , Volume: 21 Issue: 10 , Oct. 1999, Page(s): 1081 -1088.
 W. Zhao, R. Chellappa, A. Rosenfeld, and J. Phillips, “Face Recognition: A Literature Survey” . ACM Computing Surveys, Vol. 35, No. 4, December 2003, pp. 399–458.
 Face recognition home page: http://www.face-rec.org /
 Face Recognition by Humans :Nineteen Results All Computer Vision Researchers Should Know About By Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, and Richard Russell
 W. Zhao, R. Chellappa, and A. Rosenfeld, “Face recognition: a literature survey”. ACM Computing Surveys , Vol. 35:pp. 399–458, December 2003.
 J.E. Meng, W. Chen and W. Shiqian, “Highspeed face recognition based on discrete cosine transform and RBF neural networks” ; IEEE Transactions on Neural Networks, Vol. 16, Issue 3, Page(s):679 – 691, May 2005.
 Fast Face Recognition Karl B. J. Axnick1 and Kim C. Ng11 Intelligent Robotics Research Centre (IRRC), ARC Centre for Perceptive and Intelligent Machines in Complex Environments (PIMCE) Monash University, Melbourne, Australia.