A Feature Vector Compression Approach for Face Recognition using Convolution and DWT

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

Multi scale ICA based iris recognition using BSIF and Hog

Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation (HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database image with test image final features to compute performance parameters. It is observed that the performance of the proposed method is better compared to existing methods

Convolution based Face Recognition using DWT and feature vector compression

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

Face Recognition based on SWT and Procrustes Analysis

The face images are physiological biometric traits and are acquired with simple cameras for recognition of a person for security issues. In this paper, we propose face recognition based on SWT and Procrustes analysis. The face database is created by considering a number of persons with many images per person. The face images are resized to 128* 128. One frontal image from a number of image samples of a person is considered as a reference image. The Stationary Wavelet Transform (SWT) is applied on the frontal reference image and also non-frontal images of a person. The Procrustes Analysis is performed on nonfrontal images with respect to frontal reference images to convert non-frontal to frontal images. The SWT matrices are converted into row vectors. The SWT row vector of frontal reference images is concatenated with SWT row vectors of nonfrontal images to obtain final features. The Euclidean Distance (ED) is used to compare final features of database and test images to compute performance parameters. It is observed that the performance of the proposed method is better compared to existing methods