Abstract
Abstract Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.
Highlights
The first iris recognition system was proposed by Daugman in 1993 and is still the state-of-the-art technique used today [1,2]
19.31%, 19.87%, and 22.84% false non-matches of intra-class comparisons can be reduced when using Gabor phase features on three iris datasets, respectively. It means that all kinds of useful iris feature extraction should be performed only when the region of interest (ROI) region may be functioned well and the redundant eyelids and eyelashes can be excluded from the stage
This system uses two types of Gabor features generated by dividing the Gabor response magnitude and phase to represent an iris
Summary
The first iris recognition system was proposed by Daugman in 1993 and is still the state-of-the-art technique used today [1,2]. Wildes’ iris biometric method is another important approach [3,4]. The commercial iris recognition system still has problems, such as intra-class variations (e.g., iris texture affected by ageing), inter-class similarities (leads to false acceptance), and noise in data (e.g., illumination effect to iris image pixels) [8]. Feature extraction is a crucial stage for addressing these problems [9]. Two options exist to improve iris recognition performance. One option is to find effective and fast iris representation for various acquisition invariant feature transform (SIFT) [13]. Several key problems, such as illumination variations, environmental conditions, and device variations, cannot be fully addressed using a single form of iris feature
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