Improved Iris Recognition in 2D Eigen Space

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

et al. 2014

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In this piece of work a wrist vein pattern recognition and verification system is proposed. Here the wrist vein images from the PUT database were used, which were acquired in visible spectrum. The vein image only highlights the vein pattern area so, segmentation was not required. Since the wrist's veins are not prominent, image enhancement was performed. An Adaptive Histogram Equalization and Discrete Meyer Wavelet were used to enhance the vessel patterns. For feature extraction, the vein pattern is characterized with Dense Local Binary Pattern (D-LBP). D-LBP patch descriptors of each training image are used to form a bag of features, which was used to produce the training model. Support Vector Machines (SVMs) were used for classification. An encouraging Equal Error Rate (EER) of 0.79% was achieved in our experiments.

“…Various biometric techniques have been proposed in literature. Among them iris [1,2], face [3] and finger print [4] are the most efficient and promising in the literature of biometrics.…”

Section: Introductionmentioning

confidence: 99%

A new wrist vein biometric system

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

et al. 2014

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“…Adaptiveness of ocular biometrics is yet to be addressed in the literature. Among ocular biometrics, iris [26,27] is the most promising one. Apart from iris, the human eye has an ocular white surface known as the sclera, which contains a texture pattern due to the presence of blood vessels on its surface.…”

Section: Whether Co-training Can Outperform Self-training?mentioning

confidence: 99%

A framework for liveness detection for direct attacks in the visible spectrum for multimodal ocular biometrics

Pattern Recognition Letters

et al. 2016

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In the last decade adaptive biometrics has become an emerging field of research. Considering the fact that limited work has been undertaken on adaptive biometrics using machine learning techniques, in this chapter we list and discuss a few out of many potential learning techniques that can be applied to build an adaptive biometric system. In order to illustrate the efficacy of one of the incremental learning techniques from the literature, we built an adaptive biometric system. For experimentation we have used multimodal ocular (sclera and iris) data. The preliminary results have been reported in the results section, which are very promising.

“…Till date various biometric systems have been proposed, such as digital fingerprints, retinal scans, iris [26,27], facial characteristics [28], gait, and vocal patterns, these biometric characteristics are distinctive to each and every person. Though they are considered more reliable and capable than the traditional token-based or knowledge-based technologies in authenticating an individual, but they are not adaptive to change in environmental condition or change in biometric trait.…”

Section: Introductionmentioning

confidence: 99%

A new efficient and adaptive sclera recognition system

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

et al. 2014

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In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional Pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.