Automatic Personal Identification Using Feature Similarity Index Matching

Problem statement: Biometrics based personal identification is as an effective method for automatically recognizing, a person’s identity with high confidence. Palmprint is an essential biometric feature for use in access control and forensic applications. In this study, we present a multi feature extraction, based on edge detection scheme, applying Log Gabor filter to enhance image structures and suppress noise. Approach: A novel Feature-Similarity Indexing (FSIM) of image algorithm is used to generate the matching score between the original image in database and the input test image. Feature Similarity (FSIM) index for full reference (image quality assurance) IQA is proposed based on the fact that Human Visual System (HVS) understands an image mainly according to its low-level features. Results and Conclusion: The experimental results achieve recognition accuracy using canny and perwitt FSIM of 97.3227 and 94.718%, respectively, on the publicly available database of Hong Kong Polytechnic University. Totally 500 images of 100 individuals, 4 samples for each palm are randomly selected to train in this research. Then we get every person each palm image as a template (total 100). Experimental evaluation using palmprint image databases clearly demonstrates the efficient recognition performance of the proposed algorithm compared with the conventional palmprint recognition algorithms.