Efficient Human Face Recognition in Real-Life Applications using the
Discrete Wavelet Transformation (HFRDWT)
Abstract
Human Face receives major attention and acquires most of the efforts of
the research and studies of Machine Learning in detection and
recognition. In real-life applications, the problem of quick and rapid
recognition of the Human Face is always challenging researchers to come
out with powerful and reliable techniques. In this paper, we proposed a
new human face recognition system using the Discrete Wavelet
Transformation named HFRDWT. The proposed system showed that the use of
Wavelet Transformation along with the Convolutional Neural Network to
represent the features of an image had significantly reduced the face
recognition time, which makes it useful in real-life areas, especially
in public and crowded places. The Approximation coefficient of the
Discrete Wavelet Transformation played the dominant role in our system
by reducing the raw image resolution to a quarter while maintaining the
high level of accuracy rate that the raw image had. Results on ORL,
Japanese Female Facial Expression, extended Cohn-Kanade, Labeled Faces
in the Wild datasets, and our new Sudanese Labeled Faces in the Wild
dataset showed that our system obtained the least recognition timing
(average of 24 milliseconds for training and 8 milliseconds for testing)
and acceptable high recognition rate (average of 98%) compared to the
other systems.