چکیده (انگلیسی):

The development of research in the field of real-time face recognition is a study that is being developed
in the last decade. Face recognition is used to identify person from an image or video. Recognition rate
and computation time of real-time face recognition is one of the big challenges that must be developed.
This study proposes a model of face recognition using the method of feature extraction by combining
three level wavelet decomposition and Principal Component Analysis (PCA) and using the method of
mahalanobis distance for the classification section (3WPCA-MD). A 3-level wavelet decomposition is
used to decompose images by reducing the resolution used for those images. Using wavelet
decomposition up to level 3 will produce an image with a very low resolution so as to reduce the value of
the resulting computation time to be processed using PCA. Mahalanobis distance method is used to
determine the degree of similarity among the features to produce a more optimal face recognition. Based
on the results of experiments that have been done, they generated improved face recognition with high
face recognition accuracy of up to 96% in average and produced faster computation results of face
recognition if compared to ordinary PCA method. The average computation speed value obtained using
the method of 3WPCA-MD was 5-7 milli-second (ms) for each face recognition process.