Face Recognition is important Biometric credentials for identification or verification of a person. In this paper, we propose a novel technique of generating compressed unique features of face images which helps in improving matching speed of recognition. The training face database samples are applied to 2D-DWT to obtain LL band features. The LL band features are subjected to normalization to scale the magnitude values in the range 0 to 1. The output of normalization is further convolved with the original face sample to obtain unique features. The convolved output is subjected to Gaussian filter to obtain smoothened image features. Further, The feature vector of several image samples of single person are compressed to convert into single vector to database feature vectors are created by compressing feature vectors of single person face samples in to single column unique vectors which helps in scaling down of feature vectors and improve matching speed. The test samples are subjected to same process to generate unique compressed test feature vectors and are compared with database vectors using Euclidean distance. The results are tabulated for different set of face databases and also compared with existing techniques to validate the performance of proposed method.
The biometric identification of a person using face trait is more efficient compared to other traits as the co-operation of a person is not required. In this paper, we propose a feature vector compression approach for face recognition using convolution and DWT.The one level DWT is applied on face images and considered only LL band. The normalized technique is applied on LL sub band to reduce high value coefficients into lower range of values ranging between Zero and one. The novel concept of linear convolution is applied on original image and LL band matrix to enhance quality of face images to obtain unique features. The Gaussian filter is applied on the output of convolution block to reduce high frequency components to generate fine-tuned feature vectors. The numbers of feature vectors of many samples of single person are converted into a single vector which reduces number of features of each person. The Euclidean distance is used to compare test image features with features of database persons to compute performance parameters. It is observed that the performance recognition rate is high compared to existing techniques.
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