aoming Xi

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 with Personalized Feature Selection

Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.

Retinal identification based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform.

Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT), which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF) is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.

Exploring soft biometric trait with finger vein recognition

Soft biometric trait has been used as ancillary information to enhance the recognition accuracy for face, fingerprint, gait, iris, etc. In this paper, we present a new investigation of soft biometric trait to improve the performance of finger vein recognition. We first propose some extraction criteria of soft biometric trait for comprehensively understanding this kind of ancillary information. And then based on these criteria, the width of phalangeal joint is employed as a novel soft biometric trait, which can be directly extracted from finger vein image. Finally, three frameworks are developed to conduct the combination of the width measurement and finger vein pattern, i.e., the fusion framework, the filter framework and the hybrid framework. We perform rigorous experiments both on the open and self-built finger vein databases, and experimental results illustrate that soft biometric trait can make promising improvement of finger vein recognition performance.

Finger vein recognition based on the hyperinformation feature

Abstract. The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.

Finger vein verification based on a personalized best patches map

Finger vein pattern has become one of the most promising biometric identifiers. In this paper, we propose a robust finger vein verification method based on a personalized best patches map (PBPM). Firstly, some robust and discriminative visual words of finger vein are learned from traditional base feature such as local binary pattern (LBP). These visual words are named as finger vein textons (FVTs), which can well represent the visual primitives of finger vein. Secondly, we represent the finger vein image as a finger vein textons map (FVTM) by mapping each patch of the image into the closest FVT. Thirdly, by rejecting inconsistent patches, the PBPM of a certain individual is learned from these FVTMs which are extracted from the training samples of the same finger. Finally, the matched best patch ratio is used to measure similarity between the extracted FVTM of the input finger and the PBPM of a certain individual. Experimental results show that our method achieves satisfactory performance on the open PolyU database. In addition, it also has strong robustness and high accuracy on the self-built rotation and translation databases.

Finger vein recognition with superpixel-based features

Finger veins based biometrics, as a new approach to personal identification, has received much attention in recent years. The methods based on low level feature, for instance the gray, texture of finger vein, are the mainstream, but they are usually faced with many challenges, such as sensitivity to noise and low local consistency. In fact, finger vein recognition based on high level feature representation has been proved to be a promising way to effectively overcome the above limitations and improve the system performance. Thus, in this paper, we present a novel identification framework, which utilizes superpixel-based features (SPFs) of finger vein for high level feature representation. When comparing two finger veins, the features of each pixel are firstly extracted as base attributes by traditional way. Then, after superpixel over-segmentation, the SPF of each finger vein can be obtained based on its base attributes by some statistical techniques. Lastly, a weighted spatial pyramid matching (WSPM) scheme is utilized to implement matching. Our experiments have yielded some very good results evidenced by an EER of 0.0147 on the benchmark database PolyU.

Finger Vein Verification with Vein Textons

Finger vein pattern has become one of the most promising biometric identifiers. In this paper, a robust method based on Bag-of-Words (BoW) is developed for finger vein verification. Firstly, some robust and discriminative visual words are learned from local base features such as Local Binary Pattern (LBP), Mean Curvature and Webber Local Descriptor (WLD). We name these visual words as Finger Vein Textons (FVTs). Secondly, each image is mapped into a FVTs matrix. Finally, spatial pyramid matching (SPM) method is applied to maintain spatial layout information by representing each image as pyramid histogram which is performed for matching by histogram intersection function. Experimental results show that the proposed method achieves satisfactory performance both on our database and the open PolyU database. In addition, our method also has strong robustness and high accuracy on the self-built rotation and illumination databases.

Finger-Vein Recognition Based on Fusion of Pixel Level Feature and Super-Pixel Level Feature

Finger-vein is a promising biometric technique for the identity authentication. However, the finger displacement or the illumination variation in image capturing may cause bad recognition performance. To overcome these limitations, multi-biometric system, an effective method to improve the performance, is proposed. In this paper, a new multimodal biometric system based on pixel level feature and super-pixel level feature is proposed. First, the pixel level feature and the super-pixel level feature are extracted and matched by the Euclidean distance respectively. Then, pixel-super-pixel fusing score (PSPFS) is generated by the weighted fusion strategy. At last, the PSPFS is used to make the decision. Experimental results show that the proposed fusion method not only has better performance than the methods using single level feature, but also outperforms the fusion methods based on the fusion of two pixel level features.

Mining Sequential Update Summarization with Hierarchical Text Analysis

The outbreak of unexpected news events such as large human accident or natural disaster brings about a new information access problem where traditional approaches fail. Mostly, news of these events shows characteristics that are early sparse and later redundant. Hence, it is very important to get updates and provide individuals with timely and important information of these incidents during their development, especially when being applied in wireless and mobile Internet of Things (IoT). In this paper, we define the problem of sequential update summarization extraction and present a new hierarchical update mining system which can broadcast with useful, new, and timely sentence-length updates about a developing event. The new system proposes a novel method, which incorporates techniques from topic-level and sentence-level summarization. To evaluate the performance of the proposed system, we apply it to the task of sequential update summarization of temporal summarization (TS) track at Text Retrieval Conference (TREC) 2013 to compute four measurements of the update mining system: the expected gain, expected latency gain, comprehensiveness, and latency comprehensiveness. Experimental results show that our proposed method has good performance.