Research On The Iris Recognition Algorithms Of Multiple Feature Extraction And Fusion Based On Gabor Filtering

Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of humankind. Their filtered coefficients thus are widely utilized to represent the texture information of irises.In this paper, we focus on the domain of iris texture representations and matching, and obtain the following achievements:1. In our iris system, a Particle Swarm Optimization and Boolean Particle Swarm Optimization based algorithm is proposed to train better suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Our system has the advantages of adaptively tuning Gabor parameters, embedded richer informative texture in features. Some comparative experiments on JLUBR-IRIS, CASIA-I and CASIA-V4-Interval iris datasets are conducted, whose results show that our works can generate more excellent local Gabor features by optimized Gabor filters for each dataset.2. This paper has introduced an iris recognition system via multiple local Gabor feature extraction. This system uses two types of Gabor features generated by dividing Gabor responses magnitude and phase to represent iris. The local Gabor response magnitude is the model of orientation for the selective neuron in the primary visual cortex, while the local Gabor phase can capture the information from the wavelet’s zero-crossing3. This paper provides multiple feature representations and their fusion scheme based on Support Vector Regression for iris recognition. All matching scores from multiple local Gabor features are sent into a trained SVR model, and are mapped to a single scalar score to make the final decision. The output of SVR obtained a lower value demonstrate that the test iris and the involved enrollment are in a same pattern class. In light of this principle, a reasonable threshold should be chosen to make the classification decision. Our score fusion by SVR model is superior to other single feature methods in terms of both DI and ROC curves.In this paper, we also compare proposed method with other algorithms and prove its validity and superiority.4. In this paper, we try to improve the characteristics of bionic Gabor representations of each iris via combining the local Gabor features and the key-point descriptors of scale invariant feature transformation (SIFT). A SIFT key point selection strategy is provided to remove the noises and probable misaligned key points. For the combination of these iris features, we propose a support vector regression based fusion rule, which may fuse their matching scores to a scalar score to make classification decision. The experiments on three public and self-developed iris datasets validate the discriminative ability of our multiple bionic iris features, and also demonstrate that the fusion system outperform some state-of-the-art methods.5. Multi-modal biometric system has been considered as a promising technique to overcome the defects of uni-modal biometric systems. In this paper, we have introduced a fusion scheme to gain a better representation and fusion way for face-iris-fingerprint multi-modal biometric system. In our cases, we use Particle Swarm Optimization to train a set of adaptive Gabor filters in order to achieve proper Gabor basic functions for each modality. For closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next all matching scores of the two Gabor features for each modality are projected to a single scalar score via a trained Supported Vector Regression model for final decision. A large-scale combined dataset is formed to validate proposed scheme using FERET-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multi-modalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for Face-Iris-Fingerprint multi-modal biometric systems.6. In this paper, an adaptive Gabor filter selection strategy and deep feature learning scheme are presented. We first employ Particle Swarm Optimization rule and its binary version to determine a set of data-driven Gabor kernels for detecting the most informative filtering bands of iris samples involved. Moreover, the further adaptive learning features are also generated by a trained deep belief network to capture complex pattern from the optimal Gabor filtered coefficients. A succession of comparative experiments validate that our optimal Gabor filters produce more distinctive Gabor coefficients and our learned features be more robust and stable on the public CASIA-V4-Interval dataset, CASIA-V4-Lamp dataset and our self-developed larger scale JLUBR-IRIS dataset. Furthermore, the depth and scales of the deep learning architecture are also discussed in this paper.7. In video sequence-based iris recognition system, the challenge of multimodal fusion to make much of relationship and correlation among frames to help recognition still remains to be solved. A brand new template level multimodal fusion algorithm inspired by human being cognition manner is proposed. In that a connected, successive and non-isolated geometrical manifold, named Hyper Sausage Chain due to its sausage shape, is trained at feature space for representing an iris class as a template. The manifold can be utilized to recognize an input iris by observing it in the coverage of the manifold or not. This process is closer to the function of human being to cognize a new object, as its basic principle takes’matter cognition’ instead of ’matter classification’. The experiments on self-developed JLUBR-IRIS dataset including several video sequences per person, CASIA-I and CASIA-V4-Interval demonstrate the effectiveness and usability of our proposed algorithm. Furthermore, our method also can achieve improved performance in image-based iris recognition system provided enough samples involved in training.