Hand Biometrics Research Articles

Analysing Factors Affecting Hand Biometrics during Image Capture

As more people are connected digitally, a highly automatic personal identification system is crucial. Dorsal hand vein biometric is an emerging biometric characteristic which is explored at its full swing. Although, researchers have deployed many hand biometrics using interesting techniques, it has not yet been accepted in many applications. Images capture is an important phase where the images obtained determine the performance of the biometric security system. Environmental factors and behavior of the subjects have an effect on image capture. In these work, different variables, that is, distance between camera and hand, the angle of deviation and the environmental temperature are controlled to capture images. The results are analysed and the effect of the variables have been depicted. It is deduced that image capture phase in biometric applications deserve more attention.

An approach to SWIR hyperspectral hand biometrics

Hand based biometry includes some of the most useful technologies for person identification. The search for new techniques, which complement the battery of existing methods, is an open topic. This paper examines the utility of hyperspectral imagery for hand recognition. Hyperspectral technology permits the sensing of the subsurface tissue structure, which is significantly different from person to person. The data are collected using a SWIR camera in conjunction with an optical spectrograph. This transforms the camera into a line-scan hyperspectral imaging device.Three feature extraction methods for hyperspectral hand curve characterization are examined. They are based on the area, slope or curvature at different automatically selected spatial hand positions. We report a set of experiments which explore: best hand zones for extracting local hyperspectral features; robustness against the number of training samples; error detection; and occlusion. Different strategies for combining the spectral features with geometric traits available in the hyperspectral cube are discussed. Our experiments show that local spectral properties of human tissue are effective discriminants for biometric recognition with a performance near to or better than that obtained by other hand traits. Equal Error Rates of 0.05% and an identification rate of 96.71% are obtained from a database of 154 people. These results along with their higher robustness to spoofing attacks make the hyperspectral features a promising alternative for person identification.

Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%.

Assessment of geometric features for individual identification and verification in biometric hand systems

This paper studies the reliability of geometric features for the identification of users based on hand biometrics. Our methodology is based on genetic algorithms and mutual information. The aim is to provide a system for user identification rather than a classification. Additionally, a robust hand segmentation method to extract the hand silhouette and a set of geometric features in hard and complex environments is described. This paper focuses on studying how important and discriminating the hand geometric features are, and if they are suitable in developing a robust and reliable biometric identification. Several public databases have been used to test our method. As a result, the number of required features have been drastically reduced from datasets with more than 400 features. In fact, good classification rates with about 50 features on average are achieved, with a 100% accuracy using the GA-LDA strategy for the GPDS database and 97% for the CASIA and IITD databases, approximately. For these last contact-less databases, reasonable EER rates are also obtained.

Wide band spectroscopic skin detection for contactless hand biometrics

Spoofing is a real concern with regard to the security of biometric systems, especially in personal identification systems based on contactless hand biometrics which can be easily fooled with a simple hand picture. This study proposes a new approach to increase the security of contactless biometric devices using a skin detector based on reflectance spectroscopy in visual, near infrared and short wave infrared bands. The proposal is evaluated with a set of genuine and synthetic hands made from different potentially spoofing materials.

On the feasibility of interoperable schemes in hand biometrics.

Personal recognition through hand-based biometrics has attracted the interest of many researchers in the last twenty years. A significant number of proposals based on different procedures and acquisition devices have been published in the literature. However, comparisons between devices and their interoperability have not been thoroughly studied. This paper tries to fill this gap by proposing procedures to improve the interoperability among different hand biometric schemes. The experiments were conducted on a database made up of 8,320 hand images acquired from six different hand biometric schemes, including a flat scanner, webcams at different wavelengths, high quality cameras, and contactless devices. Acquisitions on both sides of the hand were included. Our experiment includes four feature extraction methods which determine the best performance among the different scenarios for two of the most popular hand biometrics: hand shape and palm print. We propose smoothing techniques at the image and feature levels to reduce interdevice variability. Results suggest that comparative hand shape offers better performance in terms of interoperability than palm prints, but palm prints can be more effective when using similar sensors.

Unconstrained and Contactless Hand Geometry Biometrics

This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices.

Personal Identification Using Multibiometrics Rank-Level Fusion

This paper investigates a new approach for the personal recognition using rank-level combination of multiple biometrics representations. There has been very little effort to study rank-level fusion approaches for multibiometrics combination and none using multiple palmprint representations. In this paper, we propose a new nonlinear rank-level fusion approach and present a comparative study of rank-level fusion approaches, which can be useful in combining multibiometrics fusion. The comparative experimental results from the publicly available multibiometrics scores and real hand biometrics data to evaluate/ascertain the rank-level combination using Borda count, logistic regression/weighted Borda count, highest rank method, and Bucklin method are presented. Our experimental results presented in this paper suggest that significant performance improvement in the recognition accuracy can be achieved as compared to those from individual palmprint representations. The rigorous experimental results presented in this paper also suggest that the proposed nonlinear rank-level approach outperforms the rank-level combination approaches presented in this paper.

An approach to hand biometrics in mobile devices

This paper focuses on hand biometrics applied to images acquired from a mobile device. The system offers the possibility of identifying individuals based on features extracted from hand pictures obtained with a low-quality camera embedded on a mobile device. Furthermore, the acquisitions have been carried out regardless illumination control, orientation, distance to camera, and similar aspects. In addition, the whole system has been tested with an owned database. Finally, the results obtained (6.0% ± 0.2) and the algorithm structure are both promising in relation to a posterior mobile implementation.

A 3D face and hand biometric system for robust user-friendly authentication

A complete authentication system based on fusion of 3D face and hand biometrics is presented and evaluated in this paper. The system relies on a low cost real-time sensor, which can simultaneously acquire a pair of depth and color images of the scene. By combining 2D and 3D facial and hand geometry features, we are able to provide highly reliable user authentication robust to appearance and environmental variations. The design of the proposed system addresses two basic requirements of biometric technologies: dependable performance under real-world conditions along with user convenience. Experimental evaluation on an extensive database recorded in a real working environment demonstrates the superiority of the proposed multimodal scheme against unimodal classifiers in the presence of numerous appearance and environmental variations, thus making the proposed system an ideal solution for a wide range of real-world applications, from high-security to personalization of services and attendance control.

Lebanese palace uses biometric hand readers

Lebanese palace uses biometric hand readers