Artificial Intelligent Systems and Machine Learning

Nilmani Verma ¹, Sanjay Kumar ², B. D. Diwan ³, Tarun Dhar Diwan ⁴

Affiliations
1 Department of Computer Science & Engineering, MAT'S University, Raipur, IN
2 Department of Information Technology, Shankaracharya College of Engineering & Technology, Bhilai, IN
3 Department of Physics, MAT'S University, Raipur, IN
4 Department of Computer Science, Shankaracharya College of Engg. & Technology, Bhilai, IN

Abstract

A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. This paper presents the design and analysis of a face recognition system using radial basis function (RBF) networks. Principal component analysis (PCA) is used as feature extraction method. PCA gives the eigenfaces. Eigenfaces are a set of eigen vectors of an image’s pixels values. The system is developed on MATLAB7 and tested for AT&T image database.

Keywords

Eigenfaces, Eigenvector, Neurons, Classifier.

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