Recognition of Eye Characteristics

This chapter deals with the recognition of features contained within the human eye, namely the iris and retina. The great advantage is that both the iris and retina contain a large amount of information, that is, they can be used for a larger group of users. The disadvantage, on the other hand, is the fear from users in regard to possible eye injury. Both of these features cannot be easily acquired and misused to cheat a biometric system. This chapter also explains how to capture and process these two biometric characteristics. However, the number of biometric industrial solutions dealing with retina recognition is very limited—it is practically not possible to find an available biometric device for identity recognition on the market based on this biometric characteristic

Recognition of Eye Characteristics

Children grow and develop in a life colored by the violation of others right, crime, compulsion, ignorance, unclearness between right and wrong, good and bad, allowed and not allowed behaviors. Building moral intelligence is very important to do in order that the childrens intuition is able to differentiate the right and the wrong. Thus, they can reject the bad influences from outside. One of the ways used to give moral value to the children is sociodrama.The research aims to know the sociodrama method in improving the moral intelligence of children. Subject of the research is the student of elementary school. The number of subject in the experiment and control groups is same that is 14 students.The research is design using model of The Untreated Control Group Design with Pretest and Posttest. The design uses two groups examined which consist of an experiment group and a control group. The measurement is conducted twice using moral intelligence measurement instrument, namely before it is given treatment (pre-test) and after it has been given treatment (post-test).The result of analysis using T-Test shows that there is a difference of moral intelligence achievement level of the children between those who receive moral value guidance through sociodrama method and those who do not receive moral value guidance through sociodrama method p = 0,009 (p<0,05). The result of analysis also shows that there is difference of moral intelligence achievement level of th children before receiving moral value guidance through sociodrama method and after they have receive the moral value guidance through sociodrama method p = 0,033 (p<0,05). The result of analysis shows the great contribution of sociodrama method towards the moral intelligence of children is 30,9%

Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm

In this paper, a thermal infra red face recognition system for human identification and verification using blood perfusion data and back propagation feed forward neural network is proposed. The system consists of three steps. At the very first step face region is cropped from the colour 24-bit input images. Secondly face features are extracted from the croped region, which will be taken as the input of the back propagation feed forward neural network in the third step and classification and recognition is carried out. The proposed approaches are tested on a number of human thermal infra red face images created at our own laboratory. Experimental results reveal the higher degree performanceComment: 7 pages, Conference. arXiv admin note: substantial text overlap with arXiv:1309.1000, arXiv:1309.0999, arXiv:1309.100

Postmortem iris recognition and its application in human identification

Iris recognition is a validated and non-invasive human identification technology currently implemented for the purposes of surveillance and security (i.e. border control, schools, military). Similar to deoxyribonucleic acid (DNA), irises are a highly individualizing component of the human body. Based on a lack of genetic penetrance, irises are unique between an individual’s left and right iris and between identical twins, proving to be more individualizing than DNA. At this time, little to no research has been conducted on the use of postmortem iris scanning as a biometric measurement of identification. The purpose of this pilot study is to explore the use of iris recognition as a tool for postmortem identification. Objectives of the study include determining whether current iris recognition technology can locate and detect iris codes in postmortem globes, and if iris scans collected at different postmortem time intervals can be identified as the same iris initially enrolled. Data from 43 decedents involving 148 subsequent iris scans demonstrated a subsequent match rate of approximately 80%, supporting the theory that iris recognition technology is capable of detecting and identifying an individual’s iris code in a postmortem setting. A chi-square test of independence showed no significant difference between match outcomes and the globe scanned (left vs. right), and gender had no bearing on the match outcome. There was a significant relationship between iris color and match outcome, with blue/gray eyes yielding a lower match rate (59%) compared to brown (82%) or green/hazel eyes (88%), however, the sample size of blue/gray eyes in this study was not large enough to draw a meaningful conclusion. An isolated case involving an antemortem initial scan collected from an individual on life support yielded an accurate identification (match) with a subsequent scan captured at approximately 10 hours postmortem. Falsely rejected subsequent iris scans or "no match" results occurred in about 20% of scans; they were observed at each PMI range and varied from 19-30%. The false reject rate is too high to reliably establish non-identity when used alone and ideally would be significantly lower prior to implementation in a forensic setting; however, a "no match" could be confirmed using another method. Importantly, the data showed a false match rate or false accept rate (FAR) of zero, a result consistent with previous iris recognition studies in living individuals. The preliminary results of this pilot study demonstrate a plausible role for iris recognition in postmortem human identification. Implementation of a universal iris recognition database would benefit the medicolegal death investigation and forensic pathology communities, and has potential applications to other situations such as missing persons and human trafficking cases

Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition

This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11 - 15, 201