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

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.

MRI Brain Cancer Diagnosis Approach Using Gabor Filter and Support Vector Machine

“…Since John Daugman proposed to use a Gabor filter in image analysis tasks in 1988 [13], wide experience of its application has been obtained. The Gabor filter is used in face recognition, fingerprints and iris recognition [14], texture analysis [15,16], medical informatics [17], defects detection [2,18], video stream analysis [19,20], image segmentation [21], texture descriptor designing [3,22], and robot vision [23,24].…”