Jane You

Online palmprint identification

Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a persons identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.

Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in the context of the Retinopathy Online Challenge (ROC), a multiyear online competition for various aspects of DR detection. For this competition, we compare the results of five different methods, produced by five different teams of researchers on the same set of data. The evaluation was performed in a uniform manner using an algorithm presented in this work. The set of data used for the competition consisted of 50 training images with available reference standard and 50 test images where the reference standard was withheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. AbrA¿moff). The results obtained on the test data was submitted through a website after which standardized evaluation software was used to determine the performance of each of the methods. A human expert detected microaneurysms in the test set to allow comparison with the performance of the automatic methods. The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detection competition will remain publicly available and the website will continue accepting submissions.

Hierarchical Palmprint Identification via Multiple Feature Extraction

Biometric computing offers an effective approach to identify personal identity by using individuals unique, reliable and stable physical or behavioral characteristics. This paper describes a new method to authenticate individuals based on palmprint identification and verification. Firstly, a comparative study of palmprint feature extraction is presented. The concepts of texture feature and interesting points are introduced to define palmprint features. A texture-based dynamic selection scheme is proposed to facilitate the fast search for the best matching of the sample in the database in a hierarchical fashion. The global texture energy, which is characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination, is used to guide the dynamic selection of a small set of similar candidates from the database at coarse level for further processing. An interesting point based image matching is performed on the selected similar patterns at fine level for the final confirmation. The experimental results demonstrate the effectiveness and accuracy of the proposed method.

Detection of microaneurysms using multi-scale correlation coefficients

This paper presents a new approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR)-a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since microaneurysms are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on multi-scale correlation filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, microaneurysm candidate detection (coarse level) and true microaneurysm classification (fine level). The approach was evaluated based on two public datasets-ROC (retinopathy on-line challenge, http://roc.healthcare.uiowa.edu) and DIARETDB1 (standard diabetic retinopathy database, http://www.it.lut.fi/project/imageret/diaretdb1). We conclude our method to be effective and efficient.

Palm vein extraction and matching for personal authentication

In this paper, we propose a scheme of personal authentication using palm vein. The infrared palm images which contain the palm vein information are used for our system. Because the vein information represents the liveness of a human, this system can provide personal authentication and liveness detection concurrently. The proposed system include: 1) Infrared palm images capture; 2) Detection of Region of Interest; 3) Palm vein extraction by multiscale filtering; 4) Matching. The experimental results demonstrate that the recognition rate using palm vein is good.

Data Uncertainty in Face Recognition

The image of a face varies with the illumination, pose, and facial expression, thus we say that a single face image is of high uncertainty for representing the face. In this sense, a face image is just an observation and it should not be considered as the absolutely accurate representation of the face. As more face images from the same person provide more observations of the face, more face images may be useful for reducing the uncertainty of the representation of the face and improving the accuracy of face recognition. However, in a real world face recognition system, a subject usually has only a limited number of available face images and thus there is high uncertainty. In this paper, we attempt to improve the face recognition accuracy by reducing the uncertainty. First, we reduce the uncertainty of the face representation by synthesizing the virtual training samples. Then, we select useful training samples that are similar to the test sample from the set of all the original and synthesized virtual training samples. Moreover, we state a theorem that determines the upper bound of the number of useful training samples. Finally, we devise a representation approach based on the selected useful training samples to perform face recognition. Experimental results on five widely used face databases demonstrate that our proposed approach can not only obtain a high face recognition accuracy, but also has a lower computational complexity than the other state-of-the-art approaches.

Classification and segmentation of rotated and scaled textured images using texture tuned masks

Abstract A rotation and scale invariant texture classifier function is described for effective classification and segmentation of images involving textures of unknown rotation and scale changes. The classifier used is the texture energy associated with a mask that has been “tuned” to be both discriminant between different textures, and to be invariant to rotation and scale changes. The mask tuning scheme utilized is based on task-oriented criterion optimization via a guided random search procedure to incorporate the changes. Both a dynamic texture sample set using a two-dimensional (2D) linked list and a re-ranking procedure are applied for training. Maximum feature dispersion of inter texture classes and high feature convergence of inner texture class samples associated with other statistical measures are suggested as key criteria in training. In a study based on 15 distinct Brodatz textures it is found that: the tuning process although computationally intensive converges efficiently; the mean classifier values of the classifier for a particular texture at different orientation and different scales are tightly clustered. An objective measure of classification capability is determined by computing the standard deviation of the classifier over pure texture at definite orientation and scale. Examples are presented of the classifier function applied to the segmentation of collages of Brodatz textures, comprising regions of various orientation and scale.

Detecting Wide Lines Using Isotropic Nonlinear Filtering

Lines provide important information in images, and line detection is crucial in many applications. However, most of the existing algorithms focus only on the extraction of line positions, ignoring line thickness. This paper presents a novel wide line detector using an isotropic nonlinear filter. Unlike most existing edge and line detectors which use directional derivatives, our proposed wide line detector applies a nonlinear filter to extract a line completely without any derivative. The detector is based on the isotropic responses via circular masks. A general scheme for the analysis of the robustness of the proposed wide line detector is introduced and the dynamic selection of parameters is developed. In addition, this paper investigates the relationship between the size of circular masks and the width of detected lines. A sequence of tests has been conducted on a variety of image samples and our experimental results demonstrate the feasibility and effectiveness of the proposed method

A wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment

We present a wavelet-based, high performance, hierarchical scheme for image matching which includes (1) dynamic detection of interesting points as feature points at different levels of subband images via the wavelet transform, (2) adaptive thresholding selection based on compactness measures of fuzzy sets in image feature space, and (3) a guided searching strategy for the best matching from coarse level to fine level. In contrast to the traditional parallel approaches which rely on specialized parallel machines, we explored the potential of distributed systems for parallelism. The proposed image matching algorithms were implemented on a network of workstation clusters using parallel virtual machine (PVM). The results show that our wavelet-based hierarchical image matching scheme is efficient and effective for object recognition.

Texture-based palmprint retrieval using a layered search scheme for personal identification

This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered: feature extraction, similarity measurement and fast search for the best match of the queried image in an image database. We propose a texture-based approach for palmprint feature representation. The concept of texture energy is introduced to define both global and local features of a palmprint, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The searching is carried out in a layered fashion: the global features are first used to guide the fast selection of a small set of similar candidates from the database and then the local features are applied to determine the final output from the selected set of similar candidates. The experimental results illustrate the effectiveness of the proposed approach.