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Efficient Human Face Recognition in Real-Life Applications using the Discrete Wavelet Transformation (HFRDWT)
  • Saddam Eragi ,
  • Fatma BenSaid ,
  • Adel Alimi
Saddam Eragi
Sudan University of Science and Technology, Sudan University of Science and Technology

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Fatma BenSaid
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Adel Alimi
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Human Face receives major attention and acquires most of the efforts of the researches and studies of Machine Learning (ML) in detection and recognition. In real-life applications, the problem of quick and rapid recognition of the Human Face is always challenging the 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 (DWT) named (HFRDWT). The proposed system showed that the use of Wavelet Transformation along with the Convolutional Neural Network (CNN) 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 DWT played the dominant role in our system by reducing the raw image resolution to quarter while maintaining the high level of the accuracy rate that the raw image had. Results on ORL, Japanese Female Facial Expression (JAFFE), extended Cohn-Kanade (CK+), Labeled Faces in the Wild (LFW) datasets, and our new Sudanese Labeled Faces in the Wild (SuLFiW) dataset showed that our system obtained the least recognition timing and acceptable high recognition rate compared to the other systems.