Zhaofeng He

Toward Accurate and Fast Iris Segmentation for Iris Biometrics

Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hookes law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.

Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition

This paper describes the winning algorithm we submitted to the recent NICE.I iris recognition contest. Efficient and robust segmentation of noisy iris images is one of the bottlenecks for non-cooperative iris recognition. To address this problem, a novel iris segmentation algorithm is proposed in this paper. After reflection removal, a clustering based coarse iris localization scheme is first performed to extract a rough position of the iris, as well as to identify non-iris regions such as eyelashes and eyebrows. A novel integrodifferential constellation is then constructed for the localization of pupillary and limbic boundaries, which not only accelerates the traditional integrodifferential operator but also enhances its global convergence. After that, a curvature model and a prediction model are learned to deal with eyelids and eyelashes, respectively. Extensive experiments on the challenging UBIRIS iris image databases demonstrate that encouraging accuracy is achieved by the proposed algorithm which is ranked the best performing algorithm in the recent open contest on iris recognition (the Noisy Iris Challenge Evaluation, NICE.I).

Efficient Iris Spoof Detection via Boosted Local Binary Patterns

Recently, spoof detection has become an important and challenging topic in iris recognition. Based on the textural differences between the counterfeit iris images and the live iris images, we propose an efficient method to tackle this problem. Firstly, the normalized iris image is divided into sub-regions according to the properties of iris textures. Local binary patterns (LBP) are then adopted for texture representation of each sub-region. Finally, Adaboost learning is performed to select the most discriminative LBP features for spoof detection. In particular, a kernel density estimation scheme is proposed to complement the insufficiency of counterfeit iris images during Adaboost training. The comparison experiments indicate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.

Iris Localization via Pulling and Pushing

Iris localization is a critical module in iris recognition because it defines the inner and outer boundaries of iris region used for feature analysis. State-of-the-art iris localization methods need to implement a brute-force search of the large parameter space, which is time-consuming and sensitive to noises. This paper proposes a novel iris localization method based on a spring force-driven iteration scheme. First, the coarse localization of pupil is obtained by an AdaBoost-based iris detection method. Then the radial edge points are detected in polar coordinate, which contribute a force on the circle center based on Hookes law. Finally the center and radius of pupil and iris are refined according to the composition of forces from all points. After 3-4 iterations, the precise location of iris could be found. Experimental results show that our method is faster and more accurate than state-of-the-art iris localization methods

Boosting ordinal features for accurate and fast iris recognition

In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris representation method based on regional ordinal measure encoding is presented, which provides an over-complete iris feature set for learning. Secondly, a novel Similarity Oriented Boosting (SOBoost) algorithm is proposed to train an efficient and stable classifier with a small set of features. Compared with Adaboost, SOBoost is advantageous in that it operates on similarity oriented training samples, and therefore provides a better way for boosting strong classifiers. Finally, the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a dasiacascadepsila, by which the searching ability of iris recognition towards large-scale deployments is greatly enhanced. Extensive experiments on two challenging iris image databases demonstrate that the proposed method achieves state-of-the-art iris recognition accuracy and speed. In addition, SOBoost outperforms Adaboost (Gentle-Adaboost, JS-Adaboost, etc.) in terms of both accuracy and generalization capability across different iris databases.

Robust eyelid, eyelash and shadow localization for iris recognition

Eyelids, eyelashes and shadows are three major challenges for effective iris segmentation, which have not been adequately addressed in the current literature. In this paper, we present a novel method to localize each of them. First, a novel coarse-line to fine-parabola eyelid fitting scheme is developed for accurate and fast eyelid localization. Then, a smart prediction model is established to determine an appropriate threshold for eyelash and shadow detection. Experimental results on the challenging CASIA-IrisV3-Lamp iris image database demonstrate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.

Topology modeling for Adaboost-cascade based object detection

Several important issues involved in Adaboost-cascade learning still remain open problems. In this work, several novel ideas are proposed for improved Adaboost-cascade object detection. The most important one is the novel topology oriented Adaboost (TOBoost) algorithm. TOBoost immediately minimizes the classification error of each selected feature, and thus enables the final detector to be more discriminative and to converge more quickly. Moreover, a simple cascading scheme is presented for tuning the cascade parameters of TOBoost; and Gaussian kernel density estimation is introduced to enhance the generalization ability of TOBoost. Another important contribution is the topology modeling of Haar-like (HL) features, which reveals an interesting property of negative HL features and significantly avoids unnecessary training computations. Non-adjacent Haar-like features are consequently configured for more effective object representation. The above enhancements result in a more efficient and stable detector with fewer features. Extensive experiments in the application of iris detection are conducted and encouraging performance is achieved.

Exploring complementary features for iris recognition on mobile devices

Iris recognition on mobile devices is challenging due to a large number of low-quality iris images acquired in complex imaging conditions. Illumination variations, low resolution and serious noises reduce the distinctiveness of iris texture. This paper explores complementary features to improve the accuracy of iris recognition on mobile devices. Firstly, optimized ordinal measures (OMs) features are extracted to encode local iris texture. Afterwards, pairwise features are automatically learned to measure the correlation between two irises using the convolutional neural network (CNN). Finally, the selected OMs features and the learned pairwise features are fused at the score level. Experiments are performed on a newly constructed mobile iris database which contains 6000 images of 200 Asian subjects. Their iris images of left and right eyes are obtained simultaneously at varying standoff distances. Experimental results demonstrate OMs features and pairwise features are highly complementary and effective for iris recognition on mobile devices.

Robust 3D face recognition in uncontrolled environments

Most current 3D face recognition algorithms are designed based on the data collected in controlled situations, which leads to the un-guaranteed performance in practical systems. In this paper, we propose a Robust Local Log-Gabor Histograms (RLLGH) method to handle the uncontrolled problems encountered in 3D face recognition. In this challenging topic, large expressions and data noises are two main obstacles. To overcome the large expressions, we choose Log-Gabor features (LGF) to extract the distinctive and robust information embedded in 3D faces, which will be represented as 3D Log-Gabor faces. Data noises are summarized as distorted meshes, hair occlusions and misalignments. To overcome these problems, we introduce a robust local histogram (RLH) strategy, which takes advantage of the robustness of the accurate local statistical information. The combination of LGF and RLH leads to RLLGH. The novelties of this paper come from 1) Our work aims at studying 3D face recognition performance in uncontrolled environments; 2) We find that embedding LGF into the LVC framework leads to robustness in handling large expression variations; 3) The RLH strategy gives a promising way to solve the data noises problem. Our experiments are based on the large expression subset in FRGC2.0 3D face database and the expression subset in CASIA 3D face database. Experimental results show the efficiency, robustness and generalization of our proposed method.

Enhanced usability of iris recognition via efficient user interface and iris image restoration

In this paper, we investigate the possibility of enhancing the usability of iris recognition via exploration of the specular spots in iris images. Firstly, the spatial configuration of the specular spots in iris images is utilized to estimate the distance between the user and the camera. Based on this a friendly user interface is established to assist users for their range adjustment. Furthermore, the estimated distance is used by an adaptive image restoration scheme to restore the blurred iris image, thereby increasing the depth of field of the iris camera. Experimental results show that the proposed method significantly enhances the usability of iris recognition without noticeable computation cost.