Abstract
In this paper, a new personal identification method based on unconstrained iris recognition is presented. We apply a nontraditional step for feature extraction where a new circular contourlet filter bank is used to capture the iris characteristics. This idea is based on a new geometrical image transform called the circular contourlet transform (CCT). An efficient multilevel and multidirectional contourlet decomposition method is needed to form a reduced-length quantized feature vector with improved performance. The CCT transform provides both multiscale and multioriented analysis of iris features. Circular contourlet-like mask filters can be used with shapes just like the 2D circular-support regions in different scales and directions. A reduced recognition system is realized using a single branch of the whole decomposition bank, highlighting a system realization with lower complexity and fewer computations. In the proposed recognition system, only five out of seven elements of the gray level cooccurrence matrix are required in the creation of the feature vector, which leads to a further reduction in computations. In addition, the highly discriminative frequency regions due to the use of circular-support decompositions can result in highly accurate feature vectors, reflecting good recognition rates for the proposed system. It is shown that the proposed system has encouraging performance in terms of high recognition rates and a reduced number of elements of the feature vector. This reflects reliable and rapid recognition properties. In addition, some promising characteristics of the system are apparent since it can efficiently be realized with lower computation complexity.
Highlights
Over the last two decades, several methods have been developed for iris recognition
The results indicated that this method increased the accuracy of unconstrained iris recognition in different circumstances, highlighting an improvement in the classification ability of iris recognition systems
An efficient unconstrained iris recognition system has been proposed in this paper using the circular contourlet transform (CCT) to extract 2D anisotropic oriented features from degraded iris templates rather than using classical methods based on textural analysis wavelet transform or even the classical contourlet transform (CT)
Summary
Over the last two decades, several methods have been developed for iris recognition. Most of those methods were designed for frontal and high-quality iris images. An ordinary Haar wavelet filter was used to extract the features which were stored in a smart card to be compared against a stored data base for authentication Another algorithm that focused on rapid and accurate iris identification was presented to deal with the occluded eye images are [9]. Different processing methods for iris images captured in unconstrained environments have been proposed. Classical edge map detection (the Canny edge detector and circular Hough transform) was used with some initially added enhancement stages for greater accuracy In this approach, the addition of the two stages of clustering and enhancement increased the complexity of computations. Fei et al [21] proposed a performance improvement method for unconstrained iris recognition in different environments based on domain adaptation metric learning solved by kernel matrix calculations.
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