Jiali Cui

A new iris segmentation method for recognition

As the first stage, iris segmentation is very important for an iris recognition system. If the iris regions were not correctly segmented, there would possibly exist four kinds of noises in segmented iris regions: eyelashes, eyelids, reflections and pupil, which result in poor recognition performance. This paper proposes a new noise-removing approach based on the fusion of edge and region information. The whole procedure includes three steps: 1) rough localization and normalization, 2) edge information extraction based on phase congruency, and 3) the infusion of edge and region information. Experimental results on a set of 2,096 images show that the proposed method has encouraging performance for improving the recognition accuracy.

An iris image synthesis method based on PCA and super-resolution

It is very important for the performance evaluation of iris recognition algorithms to construct very large iris databases. However, limited by the real conditions, there are no very large common iris databases now. In this paper, an iris image synthesis method based on principal component analysis (PCA) and super-resolution is proposed. The iris recognition algorithm based on PCA is first introduced and then, iris image synthesis method is presented. The synthesis method first constructs coarse iris images with the given coefficients. Then, synthesized iris images are enhanced using super-resolution. Through controlling the coefficients, we can create many iris images with specified classes. Extensive experiments show that the synthesized iris images have satisfactory cluster and the synthesized iris databases can be very large.

Robust and fast assessment of iris image quality

Iris recognition is one of the most reliable methods for personal identification. However, not all the iris images obtained from the device are of high quality and suitable for recognition. In this paper, a novel approach for iris image quality assessment is proposed to select clear images in the image sequence. The proposed algorithm uses three distinctive features to distinguish three kinds of poor quality images, i.e. defocus, motion blur and occlusion. Experimental results demonstrate the effectiveness of the algorithm. Clear iris images selected by our method are essential to subsequent iris recognition.

Key Techniques and Methods for Imaging Iris in Focus

Automated iris recognition is a promising method for noninvasive verification of identity. How to acquire an iris image in focus is a key issue in iris recognition. Based on imaging properties of a simple lens, working principles of fixed focus and auto focus imaging systems are described. Key techniques for imaging iris in focus are discussed in this paper, such as illumination, lens design and self alignment to position the iris in the systems depth of field. According to the techniques, a clear iris acquisition system is developed and experimental results are presented in this paper

A fast and robust iris localization method based on texture segmentation

With the development of the current networked society, personal identification based on biometrics has received more and more attention. Iris recognition has a satisfying performance due to its high reliability and non-invasion. In an iris recognition system, preprocessing, especially iris localization plays a very important role. The speed and performance of an iris recognition system is crucial and it is limited by the results of iris localization to a great extent. Iris localization includes finding the iris boundaries (inner and outer) and the eyelids (lower and upper). In this paper, we propose an iris localization algorithm based on texture segmentation. First, we use the information of low frequency of wavelet transform of the iris image for pupil segmentation and localize the iris with a differential integral operator. Then the upper eyelid edge is detected after eyelash is segmented. Finally, the lower eyelid is localized using parabolic curve fitting based on gray value segmentation. Extensive experimental results show that the algorithm has satisfying performance and good robustness.

An iris detection method based on structure information

In this paper, we propose an iris detection method to determine iris existence. The method extracts 4 types of features, i.e., contrast feature, symmetric feature, isotropy feature and disconnectedness feature. Adaboost is adopted to combine these features to build a strong cascaded classifier. Experiments show that the performance of the method is promising in terms of high speed, accuracy and device independence.

An iris recognition algorithm using local extreme points

The performance of an iris recognition algorithm depends greatly on its classification ability as well as speed. In this paper, an iris recognition algorithm using local extreme points is proposed. It first detects the local extreme points along the angular direction as key points. Then, the sample vector along the angular direction is encoded into a binary feature vector according to the surface trend (gradient) characterized by the local extreme points. Finally, the Hamming distance between two iris patterns is calculated to make a decision. Extensive experimental results show the high performance of the proposed method in terms of accuracy and speed.

Cascading statistical and structural classifiers for iris recognition

Reliable human identification using iris pattern has recently gained growing interests from pattern recognition researchers. In literature of iris recognition, almost all algorithms are based on statistical information. In this paper, a structural iris image analysis method is proposed, which provides complementary information to statistical classifier. In order to save computational cost, the structural matcher is not consulted unless the statistical classifier is uncertain of its decision. At the second stage, the structural classifier may be combined with statistical classifier with different fusion strategies. The experimental results of decision-level classifiers combination are reported, which demonstrate that the cascaded classification system significantly outperforms single classifier.

Fast recursive mathematical morphological transforms

Since many mathematical morphology operations are recursive transforms of dilation and erosion, this paper proposes fast recursive transforms to reduce computational complexity. The basic idea of the method is to compute the temporary results within a series of adaptive windows and the computing is performed on specific pixels. Each step of the recursive process consists of two parts: 1) computation is limited to the specific pixels (foreground or background pixels) within a window; 2) update the window adoptively and delete those varied pixels. Extensive results show that the time complexity of the method is proportional to the number of the specific pixels.