摘要
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.
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.
