Iris Recognition Based on SIFT Features

TL;DR

This paper introduces a novel iris recognition method using SIFT features that operates under less constrained conditions and combines well with traditional methods for improved accuracy.

Contribution

The paper presents a SIFT-based iris recognition approach that does not require precise segmentation and demonstrates enhanced performance when combined with existing methods.

Findings

SIFT features improve recognition under less constrained conditions

Combining SIFT with traditional methods yields 24% better EER

Parameter analysis shows influence on recognition accuracy

Abstract

Biometric methods based on iris images are believed to allow very high accuracy, and there has been an explosion of interest in iris biometrics in recent years. In this paper, we use the Scale Invariant Feature Transformation (SIFT) for recognition using iris images. Contrarily to traditional iris recognition systems, the SIFT approach does not rely on the transformation of the iris pattern to polar coordinates or on highly accurate segmentation, allowing less constrained image acquisition conditions. We extract characteristic SIFT feature points in scale space and perform matching based on the texture information around the feature points using the SIFT operator. Experiments are done using the BioSec multimodal database, which includes 3,200 iris images from 200 individuals acquired in two different sessions. We contribute with the analysis of the influence of different SIFT parameters on the recognition performance. We also show the complementarity between the SIFT approach and a popular matching approach based on transformation to polar coordinates and Log-Gabor wavelets. The combination of the two approaches achieves significantly better performance than either of the individual schemes, with a performance improvement of 24% in the Equal Error Rate.