Iris Recognition Using Combined Statistical and Co-Occurrence Multi-Resolutional Features

Abstract

Iris recognition is one of the most reliable personal identification methods. This paper presents a novel algorithm for iris recognition encompassing iris segmentation, fusion of statistical and co-occurrence features extracted from the curvelet and ridgelet transformed images. In this work, the pupil and iris boundaries are detected by using the equation of circle from three points on its circumference. Using Canny edge detection, the iris radius value is empirically chosen based on rigorous experimentation. Eyelash removal is done by using a horizontal 1-D rank filter. Iris normalization is done by mapping the detected iris region from the polar domain to the rectangular domain and the multi-resolution transforms such as curvelet and ridgelet transforms are applied for multi-resolutional feature extraction. The classification is done using Manhattan distance (Md) and multiclass classifier with logistic function and the two results are compared. Here, the benchmark database CASIA-IRIS-V3 (Interval) is used for identification and recognition. It is observed that the ridgelet transform increases the iris recognition rate.

Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

“…Various grid search-based block sizes are used to analyze two localized ways. Figures 10,11,12,13,14,15 shows the relationships between DIs and block sizes in two localized means. From Figures 10,11,12,13,14,15, the smallest block size is not the most suitable for localized features, and if the block size is focused excessively on the minute texture, the local features will not enhance the iris texture information but will include redundant noises.…”

“…Raja Sekar et al presented a fusion method of statistical and cooccurrence features that were extracted from the curvelet and ridgelet transformed images. The Manhattan distance and the multiclass classifier with a logistic function were used to generate the final classification result [14]. Tan et al utilized ordinal measures, color analysis, texture representation, and semantic information as iris features as well as the weighted sum rule to generate the fused score for classification [15].…”

Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape

“…Several recent algorithms have been proposed for iris segmentation, feature extraction and recognition (e.g. [28]). However, because our goal was to establish the potential of combining iris and corneal features, we did not put our efforts on choosing the best iris recognition method (a choice that would be debatable anyway) but chose instead the well‐known classical method described in [13] that is briefly summarised here.…”