Commercial iris recognition systems usually acquire images of the eye in 850-nm band of the electromagnetic spectrum. In this work, the heavily pigmented iris images are captured at 12 wavelengths, from 420 to 940 nm. The purpose is to find the most suitable wavelength band for the heavily pigmented iris recognition. A multispectral acquisition system is first designed for imaging the iris at narrow spectral bands in the range of 420-940 nm. Next, a set of 200 human black irises which correspond to the right and left eyes of 100 different subjects are acquired for an analysis. Finally, the most suitable wavelength for heavily pigmented iris recognition is found based on two approaches: 1) the quality assurance of texture; 2) matching performance-equal error rate (EER) and false rejection rate (FRR). This result is supported by visual observations of magnified detailed local iris texture information. The experimental results suggest that there exists a most suitable wavelength band for heavily pigmented iris recognition when using a single band of wavelength as illumination.

This paper describes the design and implementation of a dual-eye multispectral iris capture handheld device with one camera, which consists of the following four parts: 1) capture unit; 2) illumination unit; 3) interaction unit; and 4) control unit. A multispectral iris image database is created by the proposed capture device, and then, we use the multispectral score-level fusion to further investigate the effectiveness of the proposed capture device by the 1-D Log-Gabor wavelet filter approach. Experimental results are also presented in this paper.

Image alignment is an important step in various biometric authentication applications such as palmprint recognition. Most of the existing palmprint alignment methods make use of some key points between fingers or in palm boundary to establish the local coordinate system for region of interest (ROI) extraction. The ROI is consequently used for feature extraction and matching. Such alignment methods usually yield a coarse alignment of the palmprint images, while many missed and false matches are actually caused by inaccurate image alignments. To improve the palmprint verification accuracy, in this paper, we present an efficient palmprint alignment refinement method. After extracting the principal lines from the palmprint image, we apply the iterative closest point method to them to estimate the translation and rotation parameters between two images. The estimated parameters are then used to refine the alignment of palmprint feature maps for a more accurate palmprint matching. The experimental results show that the proposed method greatly improves the palmprint recognition accuracy and it works in real time.

An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16% and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions.

2-D palmprint has been recognized as an effective biometric identifier in the past decade. Recently, 3-D palmprint recognition was proposed to further improve the performance of palmprint systems. This paper presents a simple yet efficient scheme for 3-D palmprint recognition. After calculating and enhancing the mean-curvature image of the 3-D palmprint data, we extract both line and orientation features from it. The two types of features are then fused at either score level or feature level for the final 3-D palmprint recognition. The experiments on The Hong Kong Polytechnic University 3-D palmprint database, which contains 8000 samples from 400 palms show that the proposed feature extraction and fusion methods lead to promising performance.

Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition techniques were also proposed. The 2D and 3D palmprint data can be captured simultaneously and they provide different and complementary information. 3D palmprint contains the depth information of the palm surface, while 2D palmprint contains plenty of textures. How to efficiently extract and fuse the 2D and 3D palmprint features to improve the recognition performance is a critical issue for practical palmprint systems. In this paper, an efficient joint 2D and 3D palmprint matching scheme is proposed. The principal line features and palm shape features are extracted and used to accurately align the palmprint, and a couple of matching rules are defined to efficiently use the 2D and 3D features for recognition. The experiments on a 2D+3D palmprint database which contains 8000 samples show that the proposed scheme can greatly improve the performance of palmprint verification.

This paper presents a novel facial localization method for 3D face in the presence of facial pose and expression variation. An idea of using Multi-level Partition of Unity (MPU) Implicits in a hierarchical way is proposed for reconstruction of face surface. Based on the analysis of curvature features, nose and eyeholes regions can be detected on lower level reconstructed face surface uniquely. Experimental results show that this method is invariant to pose, holes, noise and expression. The overall performance of 99.18% is achieved.

Image alignment is a crucial step in palmprint verification. However, most of the existing palmprint alignment methods use only some key points between fingers or in palm boundary to extract the region of interest (ROI), which is consequently used for feature extraction and matching. Such alignment methods can only give a coarse alignment of the palmprint images. This paper presents a new effective refinement method for palmprint alignment by adapting the iterative closest point (ICP) method to the palmprint principal lines. The proposed method offers a more accurate alignment of palmprints by correcting efficiently the shifting, rotation and scaling variations introduced in data acquisition. The experimental results show that the proposed method can greatly improve the palmprint verification accuracy in real time.

The parameterization of a 3D mesh into a planar domain requires a distortion metric and a minimizing process. Most previous work has sought to minimize the average area distortion, the average angle distortion, or a combination of these. Typical distortion metrics can reflect the overall performance of parameterizations but discount high local deformations. This affects the performance of postprocessing operations such as uniform remeshing and texture mapping. This paper introduces a new metric that synthesizes the average distortions and the variances of both the area deformations and the angle deformations over an entire mesh. Experiments show that, when compared with previous work, the use of synthesized distortion metric performs satisfactorily in terms of both the average area deformation and the average angle deformation; furthermore, the area and angle deformations are distributed more uniformly. This paper also develops a new iterative process for minimizing the synthesized distortion, the coefficient-optimizing algorithm. At each iteration, rather than updating the positions immediately after the local optimization, the coefficient-optimizing algorithm first update the coefficients for the linear convex combination and then globally updates the positions by solving the Laplace system. The high performance of the coefficient-optimizing algorithm has been demonstrated in many experiments.

This paper presents a novel mesh simplification algorithm. It decouples the simplification process into two phases: shape analysis and edge contraction. In the analysis phase, it imposes a hierarchical structure on a surface mesh by uniform hierarchical partitioning, marks the importance of each vertex in the hierarchical structure, and determines the affected regions of each vertex at the hierarchical levels. In the contraction phase, it also divides the simplification procedure into two steps: half-edge contraction and optimization. In the first step, memoryless quadric metric error and the importance of vertices in the hierarchical structure are combined to determine one operation of half-edge contraction. In the second step, it repositions the vertices in the half-edge simplified mesh by minimizing the multilevel synthesized quadric error on the corresponding affected regions from the immediately local to the more global. The experiments illustrate the competitive results.

In this paper, we discuss two kinds of VQ algorithms. One is based on minimizing the total variance and the other is based on minimizing the maximum deviation. The algorithms of the first kind better reflect the overall fit, but may discount large, but highly localized, deviations. Those of the second kind provide absolute distance bounds that are a useful error guarantee, but may be overly sensitive to any noise that might be present in the original models. A new algorithm, combining the two criteria, is presented in this paper. It not only improves the total variance, but also provides a useful maximum error guarantee. The experiments indicate the new quantizer is a better choice in some practical operations.