Design and Simulation of Synthetic Palm Vein Image Generation

The unavailability of large-scale palm vein databases due to their intrusiveness have posed challenges in exploring this technology for large-scale applications. Hence, this research modelled and generated synthetic palm vein images from only a couple of initial samples using statistical features. Variations were introduced to the three optimized statistical features (S3; mean vectors, covariance matrices and correlation coefficient, S2; mean vectors and covariance matrices, S1; mean vectors, NS; acquired images) which were used to generate synthetic palm vein images employing Self Organizing Map (SOM) as classifier and were evaluated based on Equal Error Rate (EER), Average Recognition Accuracy (ARA) and Average Recognition Time (ART). The results obtained from the experiment showed that EERs were 0.22, 0.51, 0.58 and 4.36 for S3, S2, S1 and NS respectively. S3 had superior ARA (99.83%) compared with S2 (99.77 %), S1 (99.70 %) and NS (98.33 %). The ARTs obtained were 84.97s, 75.55s, 84.04s and 681.74s for S1, S2, S3 and NS respectively with S2 (75.55s) having significantly least value. This is on the ground that the more the optimized statistical features, the better the recognition accuracy. The research outcome justifies the extraction of mean vectors, covariance matrices and correlation coefficient with GASOM.