Author : N. Santhi 1
Date of Publication :30th March 2018
Abstract: Feature extraction is one of the most important steps in image pattern recognition. Some sources of difficulty are the presence of irrelevant information and the relativity of a feature set to a particular application. Feature extraction and description are essential components of various computer vision applications. The concept of feature extraction and description refers to the process of identifying points in an image (interesting points) that can be used to describe the image’s contents. The One major goal of feature extraction is to increase the accuracy of learned models by compactly extracting prominent features from the input data, while also possibly removing noise and redundancy from the input. Additional objectives include low-dimensional representations for data imagining and compression for the purpose of reducing data storage requirements as well as increasing training and implication speed. The aim of this paper is to report the result analysis of the most popular feature extraction techniques PCA and LDA using MATLAB to extract face features which are generally used in human recognition
Reference :
-
- Gaurav Kumar, Pradeep Kumar Bhatia, A Detailed Review of Feature Extraction in Image Processing Systems, 2014 Fourth International Conference on Advanced Computing & Communication Technologies.
- W. Zhao, R. Chellappa, A. Rosenfeld, and P. J. Phillips, ―Face recognition: A literature survey‖, Tech. Rep. CAR-TR-948, CS-TR- 4167, N00014-95-1-0521, Oct. 2000.
- R. Chellappa, S. Sirohey, C. L. Wilson, C. S. Barnes, ―Human and Machine Recognition of Faces: A Survey‖, in Proc. of IEEE, vol. 83, pp. 705-740, May 1995.
- Turk. M, Pentland. A, Eigenfaces for recognition. J Cognitive Neuro science, 1991; 3 (1):71–86.
- Lindsay I Smith, A tutorial on Principal Components Analysis, 2002.
- M. Murali, Principal Component Analysis based Feature Vector Extraction, Indian Journal of Science and Technology, Vol 8(35), 2015.
- Alaa Tharwat, Tarek Gaber, Abdelhameed Ibrahim, Aboul Ella Hassanien, Linear discriminant analysis: A detailed Tutorial, AI Communications, 2017.
- H. Yu, J. Yang, "A direct lda algorithm for highdimensional data with application to face recognition", Pattern Recognit., vol. 34, pp. 2067-2070, 2001