Ear Biometrics Based on Geometrical Feature Extraction

Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented

A Survey and Proposed Framework on the Soft Biometrics Technique for Human Identification in Intelligent Video Surveillance System

Biometrics verification can be efficiently used for intrusion detection and intruder identification in video surveillance systems. Biometrics techniques can be largely divided into traditional and the so-called soft biometrics. Whereas traditional biometrics deals with physical characteristics such as face features, eye iris, and fingerprints, soft biometrics is concerned with such information as gender, national origin, and height. Traditional biometrics is versatile and highly accurate. But it is very difficult to get traditional biometric data from a distance and without personal cooperation. Soft biometrics, although featuring less accuracy, can be used much more freely though. Recently, many researchers have been made on human identification using soft biometrics data collected from a distance. In this paper, we use both traditional and soft biometrics for human identification and propose a framework for solving such problems as lighting, occlusion, and shadowing

Beyond Biometrics

Throughout the last 40 years, the essence of automated identification of users has remained the same. In this article, a new class of biometrics is proposed that is founded on processing biosignals, as opposed to images. After a brief introduction on biometrics, biosignals are discussed, including their advantages, disadvantages, and guidelines for obtaining them. This new class of biometrics increases biometrics’ robustness and enables cross validation. Next, biosignals’ use is illustrated by two biosignal-based biometrics: voice identification and handwriting recognition. Additionally, the concept of a digital human model is introduced. Last, some issues will be touched upon that will arise when biosignal-based biometrics are brought to practice

Soft biometrics for subject identification using clothing attributes

Recently, soft biometrics has emerged as a novel attribute-based person description for identification. It is likely that soft biometrics can be deployed where other biometrics cannot, and have stronger invariance properties than vision-based biometrics, such as invariance to illumination and contrast. Previously, a variety of bodily soft biometrics has been used for identifying people. Describing a person by their clothing properties is a natural task performed by people. As yet, clothing descriptions have attracted little attention for identification purposes. There has been some usage of clothing attributes to augment biometric description, but a detailed description has yet to be used. We show here how clothing traits can be exploited for identification purposes. We explore the validity and usability of a set of proposed semantic attributes. Human identification is performed, evaluated and compared using different proposed forms of soft clothing traits in addition and in isolation

On gait as a biometric: progress and prospects

There is increasing interest in automatic recognition by gait given its unique capability to recognize people at a distance when other biometrics are obscured. Application domains are those of any noninvasive biometric, but with particular advantage in surveillance scenarios. Its recognition capability is supported by studies in other domains such as medicine (biomechanics), mathematics and psychology which also suggest that gait is unique. Further, examples of recognition by gait can be found in literature, with early reference by Shakespeare concerning recognition by the way people walk. Many of the current approaches confirm the early results that suggested gait could be used for identification, and now on much larger databases. This has been especially influenced by DARPA’s Human ID at a Distance research program with its wide scenario of data and approaches. Gait has benefited from the developments in other biometrics and has led to new insight particularly in view of covariates. Equally, gait-recognition approaches concern extraction and description of moving articulated shapes and this has wider implications than just in biometrics

Identity Management Technology

This offer comes from researchers and professors from a Spanish University. They offer solutions in Identity Management for human beings (from Identification Cards to Biometrics). They offer cooperation in R&D task, as well as Assisting in Management, Dissemination of results and Standardization. They have proved experience in both national and european projects (e.g. eEpoch, BioSec).Contrato Programa de Comercialización e Internacionalización. Sistema Regional de Investigación Científica e Innovación Tecnológica. (Comunidad de Madrid; Universidad Carlos III de Madrid