Abstract
The increased use of face recognition techniques leads to the development of improved methods with higher accuracy and efficiency. Currently, there are various face recognition techniques based on different algorithm. In this study, a new method of face recognition is proposed based on the idea of wavelet operators for creating spectral graph wavelet transformation. The proposed idea relies on the spectral graph wavelet kernel procedure. In this proposed method, feature extraction is based on transformation into SGWT by means of spatial domain. For recognition purpose, the feature vectors are used for computation of selected training samples which makes the classification. The decomposition of face image is done using the SGWT. The system identifies the test image by calculating the Euclidean distance. Finally, the study conducted an experiment using the ORL face database. The result states that the recognition accuracy is higher in the proposed system which can be further improved using the number of training images. Overall, the result shows that the proposed method has good performance in terms of accuracy of the face recognition
Highlights
There is increased usage of face recognition in modern life especially in civil security, human resource management, office administration, customer relationship management, and so on
One real valued function is used for explaining this i.e. R≥+R. the function of low pass filter is conducted by spectral graph wavelet by h(0)>0 and h(x) ≥0 as x ≥ 0 hn=()n is used for scaling function and for Sf(n)=ãn, f is used for coefficient function Proposed Methodology For face recognition, feature extraction is main step in our proposed methodology
The proposed methodology is mainly based on spectral graph wavelet theory which is considered as a filtration technique
Summary
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