IRHDF: Iris Recognition using Hybrid Domain Features

Iris Biometric is a unique physiological noninvasive trait of human beings that remains stable over a person's life. In this paper, we propose an Iris Recognition using Hybrid Domain Features (IRHDF) as Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP). An eye is preprocessed to extract the complex wavelet features to obtain the Region of Interest (ROI) area from an iris. OLBP is further applied on ROI to generate features of magnitude coefficients. Resultant features are generated by fusion of DTCWT and OLBP using arithmetic addition. Euclidean Distance (ED) is used to match the test iris image with database iris features to recognize a person. We observe that the values of Equal Error Rate (EER) and Total Success Rate (TSR) are better than in [7]

IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

Iris Recognition System based on ZM, GF, VR and Matching Level Fusion

Isis is the physiological biometric trait used to recognized a person efficiently. In this paper, we propose Iris Recognition System based on ZM, GF, VR and Matching Level Fusion. The Region of Interest (ROI) of iris is extracted using segmentation. Zernike Moments (ZM) is applied on segmented iris images to extract ZM features. The novel concept of many feature vectors of a single person are converted into single vector per person ie., Vectors Reduction (VR). The Euclidian Distance (ED) is used to compare feature vectors in the database with feature vectors in test section to compute the performance parameters. The Gabor Filter (GF) is also used to extract features of iris. Many GF feature vectors of single person are connected into single feature vector per person. The ED is used to compare database and test feature vectors to compute performance parameters. The performance parameters obtained from ZM and GF are fused using normalization technique to improve the performance parameters. It is observed that, the performance parameters are better compared to existing techniques