PATTERN RECOGNITION FROM FACE IMAGES

Year 2017, Volume: 13 Issue: 2, 14 - 20, 30.11.2017

Tolga Ensari

Abstract

In this article, we use projected gradient descent
nonnegative matrix factorization (NMF-PGD) method and make pattern recognition
analysis on ORL face data set. Face recognition is one of the critical issues
in our life and some security, daily activities and operations use this well
known application area. NMF-PGD is a type of nonnegative matrix factorization
(NMF) which defined in the literature. In the study, derived NMF-PGD definition
and algorithm has been used in order to classify the ORL face images. We give
the experimental results in a table and graph. According to experiments, face
recognition accuracy rates have different accuracy values because of the k -
lower rank value. We change k-values between 25 and 144 to see the performance
of NMF-PGD. At the end, we make some analysis and comments on the recognition
rates. Additionally, NMF-PGD can also be used for different kind of pattern
recognition problems.

Keywords

Pattern Recognition, Classification, Face Recognition, Nonnegative Matrix Factorization

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