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

Face recognition technologies for evidential evaluation of video traces

Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future

Age Sensitivity of Face Recognition Algorithms

This paper investigates the performance degradation of facial recognition systems due to the influence of age. A comparative analysis of verification performance is conducted for four subspace projection techniques combined with four different distance metrics. The experimental results based on a subset of the MORPH-II database show that the choice of subspace projection technique and associated distance metric can have a significant impact on the performance of the face recognition system for particular age groups

EEG-based biometrics: Effects of template ageing

This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vector Machines and k-Nearest Neighbours with different parametrisations. Results show that performance tends to be worse for crosssession identification compared to single session identification. This finding suggests that temporal permanence of EEG signals is limited and thus more sophisticated methods are needed in order to characterise EEG signals for the task of subject identificatio

Uncertainty Theories Based Iris Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics.