A new finger vein recognition algorithm based on Band Limited Phase Only Correlation.Finger width and Centroid Contour Distance for finger geometry recognition.The fusion of vein and geometry for a finger based bimodal biometrics system.A new infrared finger image database is made publicly available on the web. In this paper, a new approach of multimodal finger biometrics based on the fusion of finger vein and finger geometry recognition is presented. In the proposed method, Band Limited Phase Only Correlation (BLPOC) is utilized to measure the similarity of finger vein images. Unlike previous methods, BLPOC is resilient to noise, occlusions and rescaling factors; thus can enhance the performance of finger vein recognition. As for finger geometry recognition, a new type of geometrical features called Width-Centroid Contour Distance (WCCD) is proposed. This WCCD combines the finger width with Centroid Contour Distance (CCD). As compared with the single type of feature, the fusion of W and CCD can improve the accuracy of finger geometry recognition. Finally, we integrate the finger vein and finger geometry recognitions by a score-level fusion method based on the weighted SUM rule. Experimental evaluation using our own database which was collected from 123 volunteers resulted in an efficient recognition performance where the equal error rate (EER) was 1.78% with a total processing time of 24.22ms.

Fusion of Band Limited Phase Only Correlation and Width Centroid Contour Distance for finger based biometrics

Eye images provide an insight into important parts of the visual system, and also indicate the health of the entire human body. Glaucoma is one of the most common causes of blindness. It is a disease in which fluid pressure in the eye increases gradually, damaging the optic nerve and causing vision loss. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods. These methods are expensive and hence a novel low cost automated glaucoma diagnosis system using digital fundus images is proposed. The paper discusses the system for the automated identification of normal and glaucoma classes using Higher Order Spectra (HOS) and Discrete Wavelet Transform (DWT) features. The extracted features are fed to the Support Vector Machine (SVM) classifier with linear, polynomial order 1, 2, 3 and Radial Basis Function (RBF) to select the best kernel function for automated decision making. In this work, SVM classifier with kernel function of polynomial order 2 was able to identify the glaucoma and normal images automatically with an accuracy of 95%, sensitivity and specificity of 93.33% and 96.67% respectively. Finally, we have proposed a novel integrated index called Glaucoma Risk Index (GRI) which is made up of HOS and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images.

Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features

Local line directional pattern for palmprint recognition

Palmprint processes a number of unique features for reliable personal recognition. However, different types of palmprint images contain different dominant features. Instead, only some features of the palmprint are visible in a palmprint image, whereas the other features may not be notable. For example, the low-resolution palmprint image has visible principal lines and wrinkles. By contrast, the high-resolution palmprint image contains clear ridge patterns and minutiae points. In addition, the three dimensional (3-D) palmprint image possesses curvatures of the palmprint surface. So far, there is no work to summarize the feature extraction of different types of palmprint images. In this paper, we have an aim to completely study the feature extraction and recognition of palmprint. We propose to use a unified framework to classify palmprint images into four categories: 1) the contact-based; 2) contactless; 3) high-resolution; and 4) 3-D palmprint images. Then, we analyze the motivations and theories of the representative extraction and matching methods for different types of palmprint images. Finally, we compare and test the state-of-the-art methods via the widely used palmprint databases, and point out some potential directions for future research.

Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation

This paper surveys the extensive usage of pulse coupled neural networks. The visual cortex system of mammalians was the backbone for the development of pulse coupled neural network. PCNN (Pulse Coupled Neural Networks) is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. is Hence the uniqueness of this network utilized in the fields of image processing. The basic model of PCNN and the consecutive changes implemented, to strengthen the pulse coupled neural network are discussed initially. Then the applications of PCNN are broadly discussed. The other miscellaneous applications utilizing pulse coupled neural networks are thrown light in the last section.

Review: Pulse coupled neural networks and its applications

To alleviate the limitation that the recent texture based algorithms for palmprint recognition yield unsatisfactory robustness to the variations of orientation, position and illumination in capturing palmprint images, this paper describes a novel texture based algorithm for palmprint recognition combining 2D Gabor wavelets and pulse coupled neural network (PCNN). In the proposed algorithm, palmprint images are decomposed by 2D Gabor wavelets, and then PCNN is employed to imitate the creatural vision perceptive process and decompose each Gabor subband into a series of binary images. Entropies for these binary images are calculated and regarded as features. A support vector machine-based classifier is employed to implement classification. Experimental results show that the proposed approach yields a better performance in terms of the correct classification percentages and relatively high robustness to the variations of orientation, position and illumination compared with the recent texture based approaches.

Palmprint verification based on 2D - Gabor wavelet and pulse-coupled neural network

The recent on-line palmprint recognition algorithms are time-consuming, and not suitable for being implemented with hardware. This paper describes a novel on-line fast palmprint identification approach. In order to reduce the computational cost of extracting palmprint features from a palmprint image and make it easy to be implemented with hardware, we construct an adaptive lifting wavelet scheme to decompose a palmprint image into several subbands, and then the pulse-coupled neural network is employed to decompose each subband into a series of binary images. The entropies of these binary images are calculated and regarded as features. Then, in the classification step, a support vector machine-based classifier is utilized. Experimental results show that the proposed approach yields a better performance in terms of the correct classification percentages compared with the recent on-line palmprint recognition algorithms. It is also shown that the proposed approach yields observably low computational cost and can be easily implemented with hardware.

On-line fast palmprint identification based on adaptive lifting wavelet scheme

Multi-scale analysis based corner detection algorithms yield impressive performance, however, they are not efficient and not suitable for real-time computer vision tasks. The classical corner detection algorithms including FAST and Harris are computationally efficient, but their detection accuracy and repeatability are insufficient. This paper describes a novel fast corner detector with a simple architecture and high parallel computing characteristics. In order to simplify the corner detection architecture and improve its parallel computing performance, a new type of filter is proposed that can enhance corners and suppress edges and noise simultaneously. Then a novel efficient corner detector is proposed, which can be adapted to achieve real-time detection in hardware. Experimental results show that, with a very low computational cost and simple architecture, the proposed detector can achieve or even exceed the detection accuracy of multi-scale analysis based detectors. Its repeatability is similar to multi-scale analysis based detectors and clearly higher than other types of corner detectors. Therefore, it is potentially useful as an efficient corner detector for computer vision applications especially for portable real-time tasks. 

Efficient corner detection based on corner enhancement filters

Robust and Efficient Corner Detection for Computer Vision

Due to the unique image formation principle and the limitations of imaging devices, underwater images usually suffer from low contrast, color degradation, and blurring effects, which seriously hinder the interpretation of the image content. Additionally, several underwater image enhancement and restoration (UIER) algorithms have been proposed to improve the quality of underwater images. However, their performances vary greatly under different underwater scenarios. Therefore, establishing an effective quality evaluation metric plays an irreplaceable role in assessing the underwater image quality as well as evaluating the performances of UIER algorithms. In this paper, we propose a novel training-based blind underwater image quality elevation metric. Technically, the proposed metric extracts and fuses three groups of features covering naturalness, color, and contrast. Moreover, observing that the severity of color cast is highly related to the chroma of the image, we develop an underwater color factor feature to accurately estimate the severity of color distortion. Experimental results demonstrate the superior performance of the proposed method and its ability to evaluate the performance of UIER algorithms.

Mingzhe Wang

Papers

Palmprint verification based on 2D - Gabor wavelet and pulse-coupled neural network

On-line fast palmprint identification based on adaptive lifting wavelet scheme

Efficient corner detection based on corner enhancement filters

Robust and Efficient Corner Detection for Computer Vision

A Training-Based Blind Underwater Image Quality Evaluation Metric