This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT Thirty-six okra germplasms were grown and evaluated for yield and yield related traits at the Department of Plant Breeding and Genetics, Vellayani, Trivandrum, Kerala. The germplasms studied possessed sufficient variability for all the traits. High genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were noticed for almost all characters and narrow difference between GCV and PCV suggest that environmental influence is minimal for the traits studied. High estimates of heritability coupled with high to moderate genetic advance as percent over mean was recorded for all the characters considered. Yield had positive and significant association with number of fruits per plant, fruit weight, fruit girth and number of primary branches indicating that selection based on these characters may improve yield. Principal component analysis indicated that first three principal components contributed for sixty percent total variation among ten characters describing accessions. The cluster analysis revealed that hybridization of cluster I with cluster IV would be beneficial to develop promising varieties under diverse climatic conditions in India.
Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F 1 F 2 EB 1 B 2 A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a 6 × 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%.
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