Recently, both industry and academics are paying increasingly more attention to bio-metric authentication using palm vein patterns due to the benefits it provides over traditional methods, like those that rely on a fingerprint, iris, or face. However, feature extraction of the palm vein feature, which seeks to collect the most distinctive features that uniquely identify individuals might have redundant and useless features, which would reduce the performance of the bio-metric system and cause a dimension issue. This paper presents an improved feature selection technique based on a bio-inspired artificial intelligence algorithm called Discrete Artificial Bee Colony (DABC) for palm vein bio-metric authen-tication. The best set of features from those offered by Gabor feature extractor are selected using the DABC algorithm. In attempt to grant additional diversity and effectively explore the solution space, a variant of Opposition Based Learning (OBL) notion is implemented. The proposed approach, termed Modified Opposition based DABC (MODABC), has been evaluated using the CASIA palm vein database in comparison to a number of feature selection algorithms based on bio-inspired artificial intelligence. The experimental findings demonstrate that the presented method perform better that the literature in a number of areas, including accuracy, FRR, FAR, feature vector size, and fitness values. The method being proposed enables providing a suitable balance between the feature vector size and the authentication rate.

Recently, both industry and academics are paying increasingly more attention to bio-metric authentication using palm vein patterns due to the benefits it provides over traditional methods, like those that rely on a fingerprint, iris, or face. However, feature extraction of the palm vein feature, which seeks to collect the most distinctive features that uniquely identify individuals might have redundant and useless features, which would reduce the performance of the bio-metric system and cause a dimension issue. This paper presents an improved feature selection technique based on a bio-inspired artificial intelligence algorithm called Discrete Artificial Bee Colony (DABC) for palm vein bio-metric authen-tication. The best set of features from those offered by Gabor feature extractor are selected using the DABC algorithm. In attempt to grant additional diversity and effectively explore the solution space, a variant of Opposition Based Learning (OBL) notion is implemented. The proposed approach, termed Modified Opposition based DABC (MODABC), has been evaluated using the CASIA palm vein database in comparison to a number of feature selection algorithms based on bio-inspired artificial intelligence. The experimental findings demonstrate that the presented method perform better that the literature in a number of areas, including accuracy, FRR, FAR, feature vector size, and fitness values. The method being proposed enables providing a suitable balance between the feature vector size and the authentication rate.

Vein pattern recognition is one of the newest biometric technologies that competes with well-known biometric techniques such as fingerprint, face, and iris recognition. However, feature extraction of the palm vein feature, which seeks to collect the most distinctive features that uniquely identify individuals, might have redundant and useless features, which would reduce the performance of the biometric system and cause a dimension issue. This paper presents an improved feature selection technique based on a bio-inspired artificial intelligence algorithm called Discrete Artificial Bee Colony (DABC) for palm vein biometric authentication. The best set of features from those offered by the Gabor feature extractor are selected using the DABC algorithm. A variant of the Opposition-Based Learning (OBL) concept is introduced to increase diversity and efficiently explore the search area. The proposed approach, termed Modified Opposition-based DABC (MODABC), has been evaluated using the CASIA palm vein database in comparison to full feature, Discrete Particle Swarm (DSPO), Genetic Algorithm (GA), Discrete Artificial Bee Colony (DABC), and Discrete Guided Artificial Bee Colony (GDABC) based feature selection. The experimental findings demonstrate that the proposed method performs better than the DPSO, GA, DABC, and GDABC-based feature selection by improving the accuracy from 90.81 \% to 97.61 \% and reducing the feature vector size from 3200 to 1520. The proposed method provides a suitable balance between the feature vector size and the authentication rate.