Yilong Yin

On the Class Imbalance Problem

The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class. In this case, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class since traditional classifiers seeking an accurate performance over a full range of instances. This paper reviewed academic activities special for the class imbalance problem firstly. Then investigated various remedies in four different levels according to learning phases. Following surveying evaluation metrics and some other related factors, this paper showed some future directions at last.

SAR image segmentation based on Artificial Bee Colony algorithm

Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR) images is still a challenging problem. This paper proposes a fast SAR image segmentation method based on Artificial Bee Colony (ABC) algorithm. In this method, threshold estimation is regarded as a search procedure that searches for an appropriate value in a continuous grayscale interval. Hence, ABC algorithm is introduced to search for the optimal threshold. In order to get an efficient fitness function for ABC algorithm, after the definition of grey number in Grey theory, the original image is decomposed by discrete wavelet transform. Then, a filtered image is produced by performing a noise reduction to the approximation image reconstructed with low-frequency coefficients. At the same time, a gradient image is reconstructed with some high-frequency coefficients. A co-occurrence matrix based on the filtered image and the gradient image is therefore constructed, and an improved two-dimensional grey entropy is defined to serve as the fitness function of ABC algorithm. Finally, by the swarm intelligence of employed bees, onlookers and scouts in honey bee colony, the optimal threshold is rapidly discovered. Experimental results indicate that the proposed method is superior to Genetic Algorithm (GA) based and Artificial Fish Swarm (AFS) based segmentation methods in terms of segmentation accuracy and segmentation time.

Finger vein recognition with manifold learning

Finger vein is a promising biometric pattern for personal identification in terms of its security and convenience. However, so residual information, such as shade produced by various thicknesses of the finger muscles, bones, and tissue networks surrounding the vein, are also captured in the infrared images of finger vein. Meanwhile, the pose variation of the finger may also cause failure to recognition. In this paper, for the first time, we address this problem by unifying manifold learning and point manifold distance concept. The experiments based on the TED-FV database demonstrate that the proposed algorithmic framework is robust and effective.

SDUMLA-HMT: a multimodal biometric database

In this paper, the acquisition and content of a new homologous multimodal biometric database are presented. The SDUMLA-HMT database consists of face images from 7 view angles, finger vein images of 6 fingers, gait videos from 6 view angles, iris images from an iris sensor, and fingerprint images acquired with 5 different sensors. The database includes real multimodal data from 106 individuals. In addition to database description, we also present possible use of the database. The database is available to research community through http://mla.sdu.edu.cn/sdumla-hmt.html.

Hierarchical retinal blood vessel segmentation based on feature and ensemble learning

Segmentation of retinal blood vessels is of substantial clinical importance for diagnoses of many diseases, such as diabetic retinopathy, hypertension and cardiovascular diseases. In this paper, the supervised method is presented to tackle the problem of retinal blood vessel segmentation, which combines two superior classifiers: Convolutional Neural Network (CNN) and Random Forest (RF). In this method, CNN performs as a trainable hierarchical feature extractor and ensemble RFs work as a trainable classifier. By integrating the merits of feature learning and traditional classifier, the proposed method is able to automatically learn features from the raw images and predict the patterns. Extensive experiments have been conducted on two public retinal images databases (DRIVE and STARE), and comparisons with other major studies on the same database demonstrate the promising performance and effectiveness of the proposed method. A supervised method based on feature and ensemble learning is proposed.The whole pipeline of the proposed method is automatic and trainable.Convolutional Neural Network performs as a trainable hierarchical feature extractor.Ensemble Random Forests work as a trainable classifier.Compared with state-of-the-arts, the experimental results are promising.

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition.

FINGER VEIN RECOGNITION BASED ON (2D)2PCA AND METRIC LEARNING

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2 PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.

Finger Vein Recognition Based on Local Directional Code

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.

Sliding window-based region of interest extraction for finger vein images.

Region of Interest (ROI) extraction is a crucial step in an automatic finger vein recognition system. The aim of ROI extraction is to decide which part of the image is suitable for finger vein feature extraction. This paper proposes a finger vein ROI extraction method which is robust to finger displacement and rotation. First, we determine the middle line of the finger, which will be used to correct the image skew. Then, a sliding window is used to detect the phalangeal joints and further to ascertain the height of ROI. Last, for the corrective image with certain height, we will obtain the ROI by using the internal tangents of finger edges as the left and right boundary. The experimental results show that the proposed method can extract ROI more accurately and effectively compared with other methods, and thus improve the performance of finger vein identification system. Besides, to acquire the high quality finger vein image during the capture process, we propose eight criteria for finger vein capture from different aspects and these criteria should be helpful to some extent for finger vein capture.

A Fingerprint Matching Algorithm Based On Delaunay Triangulation Net

Fingerprint matching is a key issue in research of an automatic fingerprint identification system. On the basis of Delaunay triangulation (DT) in computational geometry, we proposed a fingerprint matching algorithm based on DT net in this paper. It uses DT in fingerprint matching, and then develops a matching algorithm based on DT net to find reference minutiae pairs (RMPs). Using DT on the topological structure of minutiae set, a DT net is formed with minutiae as vertexes. From the nets of the input minutiae set and template minutiae set, select out a certain pairs of minutiae which have similar structures as RMPs for aligning, and matching is carried out based on point pattern. The experiment is conducted on FVC2002 and the result indicates the validity of our algorithm