A Finger Vein Image Quality Assessment Method Using Object and Human Visual System Index

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

Low-quality finger vein images seriously affect the performance of the recognition system. Aiming at filtering high- and low-quality finger vein images, this paper presents a finger-vein image quality assessment method based on light-CNN (convolutional neural network). The previous quality assessment method aims to establish a model that is consistent with the evaluation effect of the human visual system. In contrast, our model further evaluates the quality of the finger vein image for the purpose of distinguishing whether the image contains rich and stable vein characteristics. The rich and stable vein characteristics in finger vein images play a more important role in practical applications. First, the finger vein image is automatically annotated based on a traditional finger vein image quality assessment method; secondly, the finger vein image is cut into image blocks to expand the training set, and the image block instead of the entire image is input to the CNN; then, the network and its variants are trained; in the final test, the average quality score of multiple image blocks from an image is the final quality score of the image. The experimental results show that the proposed light-CNN accurately identifies high and low quality images and significantly outperforms existing approaches in terms of distinguishing whether there are rich and stable vein characteristics in the image.

Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

In this paper, we propose a novel quality assessment of finger-vein images for quality control in the enrollment and authentication of a finger-vein verification system. First, a Radon transform based model is employed to assess the quality of a finger-vein grayscale image. Second, to assess the quality of a finger-vein binary image, we further proposed three evaluation functions to measure the connectivity, smoothness and reliability of the binary version of the finger-vein image. Finally, the scores from the finer-vein binary images are fused with these from finger-vein grayscale images to improve the performance. Experimental results show that our approach can effectively identify the low quality finger-vein images, which is also helpful in improving the performance of the finger-vein verification system. We also show that instead of choosing the images with the highest quality as the enrollment templates, using the templates with the mid-range quality would achieve better performance with respect to improvement of varication accuracy.

Exploring finger vein based personal authentication for secure IoT

For personal identification, the biometric systems based on finger-vein pattern have been successfully used in many applications. The concern for the system efficiency over a large database should not be negligible in the real situation. So, categorizing the finger-vein images to different classes is helpful for reducing pattern matching cost. In this paper, we propose a level-based framework for roughly and automatically categorizing finger-vein images. The proposed level-based framework consists of two layers in classifying finger-vein images. In this framework, the imaging qualities and the image contents are respectively used for the first layer and second layer image feature representation. And the k-means algorithm is adopted for automatic finger-vein image clustering. Using SVM scheme, we can achieve 99% CCR (correct classification rate) over a large image database. Finally, for comparison, the POC (Phase-Only-Correction) matching algorithm is used. Experimental results show that the proposed method has a good performance in the improving recognition efficiency as well as recognition accuracy.

The identity recognition technology as an important aspect in the field of artificial intelligence. Especially with the development of intelligent robot has the function of identification, the application fields of further widening. This paper presents an identification algorithm of finger vein quality assessment based on the support vector machine. Through the analysis of some existing features, and analyses the three characteristic parameters of great influence on the finger vein image quality (image contrast, image gradient covariance feature values, and the effective area). Vein image by establishing a model of support vector machine to the known training quality, and then, classify the test image random sampling. The experimental results show that this algorithm can well distinguish the vein image high and low quality for enhance the performance of the finger vein identification system.

The modern-day finger vein based human recognition techniques provide good performance, yet they are highly finger vein image quality dependent. To address this problem, a novel deep learning-based approach using convolution-neural-network (CNN) for finger vein identification has been introduced here. The prime objective of our work is to achieve a stable response with accurate performance keeping varying quality finger vein images in account. The proposed approach is tested on the considered publicly available dataset and reported experiment results show that with effective training and testing strategy high identification accuracy can be achieved.

To better evaluate image quality,the authors proposed a top-down no-reference image objective quality evaluation method based on the research of human visual model and human visual characteristics.The new method divided the target image into segments and calculated every segment in different color channel,then output the average visual property values.It made evaluation model not only be consistent with the advantage of objective quality assessment,but also took into account the human visual experience.It was consistent with the objectivity and subjectivity of human eyes.The validated results of image subjective evaluation on database of TEXAS University of United States prove that the proposed method gets better consistency with subjective assessment.

Medical image enhancement is to process the image so that the result is more suitable than the original image for analysis and diagnosis. The information of image is acquired by the observer's eyes, so in the image processing, the human visual characteristics (HVC) as an important factor should be well thought of. In the paper, we study HVC from testing the distinguishability of gray scale and the sensitivity to the structure of image, analyze the physiological character and psychological characters of human vision, and summarize the laws of HVC. A new medical image enhancement method is presented based on HVC. It divides the image into the smooth areas and detail areas, and uses different processing methods for the areas according to HVC. Experiment results demonstrate that the method enhances the edge detail information, and improves the image quality, thereby increases the veracity of diagnoses, makes the treatment approach more exact.

In this study, a fractal dimension-based method has been developed to compute the visual complexity of the heterogeneity in the built environment. The built environment is a very complex combination, structurally consisting of both natural and artificial elements. Its fractal dimension computation is often disturbed by the homogenous visual redundancy, which is textured but needs less attention to process, so that it leads to a pseudo-evaluation of visual complexity in the built environment. Based on human visual perception, the study developed a method: fractal dimension of heterogeneity in the built environment, which includes Potts segmentation and Canny edge detection as image preprocessing procedure and fractal dimension as computation procedure. This proposed method effectively extracts perceptually meaningful edge structures in the visual image and computes its visual complexity which is consistent with human visual characteristics. In addition, an evaluation system combining the proposed method and the traditional method has been established to classify and assess the visual complexity of the scenario more comprehensively. Two different gardens had been computed and analyzed to demonstrate that the proposed method and the evaluation system provide a robust and accurate way to measure the visual complexity in the built environment.

Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> EB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a 6 × 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%.

multimodal biometric systems have been widely used to overcome the limitation of unimodal biometric systems and to achieve high recognition accuracy. However, users feel inconvenience because most of the multimodal systems require several steps in order to acquire multimodal biometric data, which also requires the specific behaviors of users. In this research, we propose a new multimodal biometric recognition of touched fingerprint and finger-vein. This paper is novel in the following four ways. First, we can get a fingerprint and a finger-vein image at the same time by the proposed device, which acquires the fingerprint and finger-vein images from the first and second knuckles of finger, respectively. Second, the device's size is so small that we can adopt it on a mobile device, easily. Third, fingerprint recognition is done based on the minutia points of ridge area and finger-vein recognition is performed based on local binary pattern (LBP) with appearance information of finger area. Fourth, based on decision level fusion, we combined two results of fingerprint and finger-vein recognition. Experimental results confirmed the efficiency and usefulness of the proposed method.

With fast growth in Internet and digital imaging knowledge, there are progressive customs to simply generate, distribute and allocate the images. Measured the association among digital imaging and privacy fortification, Visual Cryptography and Secret Image Sharing is an inclusive preamble to original security methods. The secret sharing control system is used to guard unauthorized data access and to secure distribution of responsive information. An efficient color image visual cryptic filtering scheme is to improve the image quality on restored original image from visual cryptic shares. The color image visual cryptic filtering scheme presents a deblurring effect on the non-uniform distribution of visual cryptic share pixels. Fourier transformation and texture overlapping is applied to normalize the unevenly transformed share pixels on the original restored image. This in turn improves the quality of restored visual cryptographic image to its optimality. The privacy preservation in visual cryptography plays a major role to provide an efficient secure communication for the users. To address data sharing issues more sensitively, a privacy preservation achieved by using cache-cache mechanism. The cache-cache mechanism maintains a cache inside a cache to preserve the details about the users who shared the secret parts of digital document, which has been splitted in a scheme of visual cryptography. In this work, the binary spanning trees are used to enhance the progress of secure distributed file sharing in visual cryptography. Privacy of file sharing is done with file block id relating to the participant id using binary trees. Group dynamics is handled effectively with distributed key exchange model, based on the principal operation of minimum spanning tree variants. Experimentation carried out with bench mark data sets of real and synthetically generated data sets to estimate the performance of the proposed controlled adaptable distributed file sharing with multiple participants in visual cryptography in terms of group dynamic rate, tree spanning length and privacy overhead.

Digitized hand-wrist radiographs: Comparison of subjective and software-derived image quality at various compression ratios