Hand Geometry Features Research Articles

A hierarchical identification method based on improved hand geometry and regional content feature for low-resolution hand images

Low-quality images are taking an increasing amount in real-life biometrics identification systems, especially when the working environment is gradually becoming complex and open. Low resolution is a crucial component of low quality. We propose a hierarchical identification method based on improved hand geometry and regional content feature for low-resolution hand images without region of interest (ROI) cropping. At coarse level, angle information is added as a complement to line-based hand geometry. At fine level, relying on the assumption that gradient value of each pixel presents the gray-level changing rate, we develop a simple sequence labeling segmentation method, and choose conditional regions that are relatively steady in segmentation through region area constraint. Because distinctive lines and dense textures always have lower gray-levels than their surrounding areas, regions with lower average gray-levels are selected from conditional regions. Regional centroid coordinates are extracted as feature vectors. Finally, regional spatiality relationship matrix is built up to measure distances between feature vectors with various dimensions. Compared with ROI-based state-of-the-art algorithms, the proposed method utilizes more available information in low-resolution hand images. Performance comparisons between algorithms prove that our method is effective and robust to rotation and translation.

Authentication of Individuals using Hand Geometry Biometrics: A Neural Network Approach

Biometric based systems for individual authentication are increasingly becoming indispensable for protecting life and property. They provide ways for uniquely and reliably authenticating people, and are difficult to counterfeit. Biometric based authenticity systems are currently used in governmental, commercial and public sectors. However, these systems can be expensive to put in place and often impose physical constraint to the users. This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work comprises on the introduction of hand geometry's related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research comprises on the use of error correction codes to enhance the level of performance of the neural network model. A dataset made of scanned images of the right hand of fifty different people was created for this study. Identification rates and Detection Cost Function (DCF) values obtained with the system were evaluated. Several strategies for coding the outputs of the neural networks were studied. Experimental results show that, when using Error Correction Output Codes (ECOC), up to 100% identification rates and 0% DCF can be obtained. For comparison purposes, results are also given for the Support Vector Machine method.

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.

Hand-Geometry Recognition Using Entropy-Based Discretization

The hand-geometry-based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature-level representation. We investigate the possibilities to improve the performance of the existing hand-geometry systems using the discretization of extracted features. This paper proposes employing discretization of hand-geometry features, using entropy-based heuristics, to achieve the performance improvement. The performance improvement due to the unsupervised and supervised discretization schemes is compared on a variety of classifiers: k-NN, naive Bayes, SVM, and FFN. Our experimental results on the database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in hand-geometry-based systems

Personal authentication using hand images

This paper presents a new approach for personal authentication using hand images. The proposed method attempts to improve the performance of palmprint-based verification system by integrating hand geometry features. Unlike prior bimodal biometric systems, the users do not have to undergo the inconvenience of using two different sensors in our system since the palmprint and hand geometry images are acquired simultaneously using a single camera. The palmprint and handshape images are used to extract salient features and are then examined for their individual and combined verification performances. The image acquisition setup used here is inherently simple and it does not employ any special illumination nor does it use any alignment pegs to cause any inconvenience to the users. Our experiments on an image database of 100 users achieve promising results and suggest that the fusion of matching scores can achieve better performance than the fusion at representation.

Development of Palmprint Verification System Using Biometrics

Palmprint verification system using Biometrics is one of the emerging technologies, which recognizes a person based on the principle lines, wrinkles and ridges on the surface of the palm. These line structures are stable and remain unchanged throughout the life of an individual. More importantly, no two palmprints from different individuals are the same, and normally people do not feel uneasy to have their palmprint images taken for testing. Therefore palmprint recognition offers a promising future for medium-security access control systems. In this paper, a new approach for personal authentication using hand images is discussed. Gray-Scale palm images are captured using a digital camera at a resolution of 640×480. Each of these gray-scale images is aligned and then used to extract palmprint and hand geometry features. These features are then used for authenticating users. The image acquisition setup used here is inherently simple and it does not employ any special illumination nor does it use any pegs that might cause any inconvenience to users. Experimental results show that the designed system achieves an