Hand Contour Recognition In Language Signs Codes Using Shape Based Hand Gestures Methods

The deaf and speech impaired are loosing of hearing ability followed by disability of developing talking skill in everyday communication.  Disability of making normal communication makes the deaf and speech impaired being difficult to be accepted by major normal community.  Communication used is gesture language, by using hand gesture communication. The weakness of this communication is that misunderstanding and limitation, it’s due to hand gesture is only understood by minor group.  To make effective communication in real time, it’s needed two ways communication that can change the code of hand gesture pattern to the texts and sounds that can be understood by other people. In this research, it’s focused on hand gesture recognition using shaped based hand algorithm where this method classifies image based on hand contour using hausdorff and Euclidian distance to determine the similarity between two hands based on the shortest range.  The result of this research is recognizing 26 letters gesture, the accuracy of this Gesture is 85%, from different human hands, taken from different session with different lighting condition and different range of camera from image.  It also can recognize 70% different hand contour.  The different of this research from other researches is the more the objects are, the less the classification of hands size is. Using this method, hands size can be minimized

New Mobile Phone and Webcam Hand Images Databases for Personal Authentication and Identification

AbstractIn this work we created two hand image databases, usingmobile phone cameras and webcams. Themajor goal of these databases is to build upon aperson's authentication/identification using hand biometrics,decreasing the need for expensive hand scanners. Both databases consist of 3000 hand images, 3 sessions x 5 images (per person)x 200 persons, and are available to freely download. The test protocol is defined for both databases; simple experiments were conducted using the same protocol. The results were encouraging for most of the persons (accuracy was greater than 80%), except for those who rotated their hands in an exaggerated manner in all directions

Hand Geometry Techniques: A Review

Volume 2 Issue 11 (November 2014

The Application of Hierarchical Clustering Algorithms for Recognition Using Biometrics of the Hand

In data analysis, the hierarchical clustering algorithms are powerful tools allowing to identify natural clusters, often without any priori information of the data structure, and are quite often used because provide a graphical representation of the resulting partitions, a hierarchy or dendrogram, revealing more information than non-hierarchical algorithms that returns a unique partition. Moreover, it is not necessary specify the number of clusters à priori. Cutting the dendrogram in different levels on the hierarchy produces different partitions and also, the use of different clusters aggregation methods for the same data set can produces different hierarchies and hence different partitions. So, several studies have been concerned with validate the resulting partitions comparing them, for instance, by the analysis of cohesion and separation of their clusters. The work presented here focuses on the problem of choosing the best partition in hierarchical clustering. The procedure to search for the best partition is made in the nested set of partitions, defined by the hierarchy. In traditional approaches each partition is defined by horizontal lines cutting the dendrogram at a determined level. In [3] is proposed an improved method, SEP/COP, to obtain the best partition, based on a wide set of partitions. In this paper we discuss these two types of approaches and we do a comparative study using a set of experiments using two-dimensional synthetic data sets and a real-world data set, based on the biometrics of the hands. This database is provided from Bosphorus Hand Database [36], in the context of recognition of the identity of a person by using the features of her hand/biometrics. We conclude that neither of the approaches proved consistently to perform better than the other, but the SEP/COP algorithm showed to be a better partition algorithm in situations like clusters with the approximately the same cardinality and well apart. Also, less depend of the used aggregation criteria and more robust to the presence of noise. Regarding to real data, the results of the experiments demonstrated that SEP/COP hierarchical clustering algorithms approach can contribute to identification systems based on the biometrics of the hands shape

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

Computing minimum-volume enclosing axis-aligned ellipsoids

Given a set of points S = {x1 ,..., xm}⊂ ℝn and ε>0, we propose and analyze an algorithm for the problem of computing a (1+ε)-approximation to the minimum-volume axis-aligned ellipsoid enclosing S. We establish that our algorithm is polynomial for fixed ε. In addition, the algorithm returns a small core set X ⊆ S, whose size is independent of the number of points m, with the property that the minimum-volume axis-aligned ellipsoid enclosing X is a good approximation of the minimum-volume axis-aligned ellipsoid enclosing S. Our computational results indicate that the algorithm exhibits significantly better performance than the theoretical worst-case complexity estimate. © 2007 Springer Science+Business Media, LLC

Hand-Based Biometric Analysis

Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation