TITLE:
Face Recognition Using Fuzzy Clustering and Kernel Least Square
AUTHORS:
Essam Al Daoud
KEYWORDS:
Face Recognition, Fuzzy Clustering, Kernel, Least Square, Gabor Filters
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.3,
March
17,
2015
ABSTRACT: Over the last fifteen years, face recognition
has become a popular area of research in image analysis and one of the most successful
applications of machine learning and understanding. To enhance the classification
rate of the image recognition, several techniques are introduced, modified and combined.
The suggested model extracts the features using Fourier-Gabor filter, selects the
best features using signal to noise ratio, deletes or modifies anomalous images
using fuzzy c-mean clustering, uses kernel least square and optimizes it by using
wild dog pack optimization. To compare the suggested method with the previous methods,
four datasets are used. The results indicate that the suggested methods without
fuzzy clustering and with fuzzy clustering outperform state- of-art methods for
all datasets.