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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.

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 is considered to be a very promising biometric identification technology due to its excellent recognition performance. However, in the real world, the finger vein recognition system inevitably suffers from the single-sample problem: that is, only one sample is registered per class. In this case, the performance of many classical finger vein recognition algorithms will decline or fail because they cannot learn enough intra-class variations. To solve this problem, in this paper, we propose a SIFT-flow-based virtual sample generation (SVSG) method. Specifically, first, on the generic set with multiple registered samples per class, the displacement matrix of each class is obtained using the scale-invariant feature transform flow (SIFT-flow) algorithm. Then, the key displacements of each displacement matrix are extracted to form a variation matrix. After removing noise displacements and redundant displacements, the final global variation matrix is obtained. On the single sample set, multiple virtual samples are generated for the single sample according to the global variation matrix. Experimental results on the public database show that this method can effectively improve the performance of single-sample finger vein recognition. [ABSTRACT FROM AUTHOR]

• We propose a method of generating multiple views by using single lead signal. • We introduce the sample diversity information into multi-view learning to seek the more effective discriminant subspace. • To reduce the influence of noise, we introduce a constraint to adaptively learn the relationships between different views. Currently, electrocardiogram (ECG) biometric recognition is a novel research trend, and many methods have been developed. Due to the influence of physical and psychological activities, there are heartbeats diversities of the same person. However, the existing ECG biometric recognition methods do not make use of sample diversity information. In this paper, we present a multi-view discriminant analysis approach in the consideration of sample diversity for ECG biometric recognition. Firstly, we propose a method of generating multiple views by using single lead ECG signal. Secondly, we present a multi-views learning framework, which takes sample diversity into account to generate a more discriminative subspace. Thirdly, to obtain a more robust solution, we introduce a denoising constraint to learn the relationships between different views, which can create a stable representation against ECG noise. At last, experimental results demonstrate that compared with the state-of-the-art methods on four databases, the proposed method can achieve competitive performance compared to state-of-the-art ECG biometric recognition methods. [ABSTRACT FROM AUTHOR]

Abstract Biometric human identification refers to the automated recognition of individuals based on their biological or behavioral characteristics. Finger vein recognition and electrocardiogram (ECG) recognition have received a lot of attention for over ten years and these two traits are viewed as ones of promising biometric traits because of their unique advantages. However, these two independent unimodal biometrics, which using a single biometric trait for personal recognition, usually cannot satisfy the requirements of real-world applications. Finger vein and ECG signals have their unique advantages and also have their respective shortcomings. It has been well established that multimodal biometrics systems can overcome the respective defects of unimodal approach. Thus, how to jointly exploit these two modalities and develop an effective and efficient multimodal identification system are still open problems. However, to the best of our knowledge, there has been no study on the multimodal biometric systems using finger vein and ECG signals. Therefore, in this paper we make the first attempt to integrate finger vein with ECG signals for personal identification using various fusion strategies. A new multimodal biometric method based on Discriminant correlation analysis (DCA) for the fusion of finger vein and ECG is proposed. Extensive experiments are conducted on a merged bi-modal dataset named VeinECG coming from FVPolyU finger vein dataset and ECG-ID Dataset. Experimental results show that the proposed multimodal system is substantially superior to two individual unimodal systems in terms of both recognition accuracy and security. [ABSTRACT FROM AUTHOR]

Finger vein recognition has received a lot of attention recently and is viewed as a promising biometric trait. In related methods, vein pattern-based methods explore intrinsic finger vein recognition, but their performance remains unsatisfactory owing to defective vein networks and weak matching. One important reason may be the neglect of deep analysis of the vein anatomy structure. By comprehensively exploring the anatomy structure and imaging characteristic of vein patterns, this paper proposes a novel finger vein recognition framework, including an anatomy structure analysis-based vein extraction algorithm and an integration matching strategy. Specifically, the vein pattern is extracted from the orientation map-guided curvature based on the valley- or half valley-shaped cross-sectional profile. In addition, the extracted vein pattern is further thinned and refined to obtain a reliable vein network. In addition to the vein network, the relatively clear vein branches in the image are mined from the vein pattern, referred to as the vein backbone. In matching, the vein backbone is used in vein network calibration to overcome finger displacements. The similarity of two calibrated vein networks is measured by the proposed elastic matching and further recomputed by integrating the overlap degree of corresponding vein backbones. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Finger vein pattern, as a promising hand-based biometric technology, has been well studied in recent years. In this paper, a new superpixel-based finger vein recognition method is presented. In the proposed method, we develop two types of effective superpixels, i.e. stable superpixel and discriminative superpixel to represent finger vein image and these superpixels are expected to play different roles in matching stage. In detail, the stable and discriminative superpixels are firstly learned from the training images for each enrolled class. When verifying a testing image, we just compare the superpixels at the same location as the two types of superpixels in template. Then, the two types of superpixels are combined utilizing a reversible weight-based fusion method in score level. Additionally, to further improve the recognition performance, we explore the superpixel context feature (SPCF). For each superpixel the SPCF is obtained by comparing the current superpixel with its surrounding neighbors. In the final matching stage, we integrate the matching score of two types of superpixels and it of the SPCF using the weighted SUM fusion method. The experimental results on two open finger vein databases, i.e. PolyU and SDUMLA-FV, show that our method not only performs better than the existing superpixel-based method, but also has advantages in comparison with some traditional ones. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark. [ABSTRACT FROM AUTHOR]