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"Identification (Physical)"
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Craniofacial identification
\"The promotion of CCTV surveillance and identity cards, along with ever heightened security at airports, immigration control and institutional access, has seen a dramatic increase in the use of automated and manual recognition. In addition, several recent disasters have highlighted the problems and challenges associated with current disaster victim identification. Discussing the latest advances and key research into identification from the face and skull, this book draws together a wide range of elements relating to craniofacial analysis and identification. It examines all aspects of facial identification, including the determination of facial appearance from the skull, comparison of the skull with the face and the verification of living facial images. With sections covering the identification of the dead and of the living, it provides a valuable review of the current state of play along with the latest research advances in this constantly evolving field\"-- Provided by publisher.
Book
Analytical, Experimental, and Numerical Investigation of Energy in Hydraulic Cylinder Dynamics of Agriculture Scale Excavators
This paper discusses energy behaviors in hydraulic cylinder dynamics, which are important for model-based control of agriculture scale excavators. First, we review hydraulic cylinder dynamics and update our physical parameter identification method to agriculture scale experimental excavators in order to construct a nominal numerical simulator. Second, we analyze the energy behaviors from the port-Hamiltonian point of view which provides many links to model-based control at laboratory scale at least. At agriculture scale, even though the nominal numerical simulator is much simpler than an experimental excavator, the analytical, experimental, and numerical energy behaviors are very close to each other. This implies that the port-Hamiltonian point of view will be applicable in agriculture scale against modeling errors.
Journal Article
Human identity and identification
\"Few things are as interesting to us as our own bodies and, by extension, our own identities. In recent years, there has been a growing interest in the relationship between the body, environment and society. Reflecting upon these developments, this book examines the role of the body in human identification, in the forging of identities, and the ways in which it embodies our social worlds. The approach is integrative, taking a uniquely biological perspective and reflecting on current discourse in the social sciences. With particular reference to bioarchaeology and forensic science, the authors focus on the construction and categorisation of the body within scientific and popular discourse, examining its many tissues, from the outermost to the innermost, from the skin to DNA. Synthesising two, traditionally disparate, strands of research, this is a valuable contribution to research on human identification and the embodiment of identity.\"-- Provided by publisher.
Book
Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio
Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using such technologies in real-world applications. In this research, the challenge has been addressed by reducing the complexity of the brain signal acquisition and analysis processes. This was achieved by reducing the number of electrodes, simplifying the critical task without compromising accuracy. Event-related potentials (ERP), a.k.a. time-locked stimulation, was used to collect data from each subject's head. Following a relaxation period, each subject was visually presented a random four-digit number and then asked to think of it for 10 seconds. Fifteen trials were conducted with each subject with relaxation and visual stimulation phases preceding each mental recall segment. We introduce a novel derived feature, dubbed Inter-Hemispheric Amplitude Ratio (IHAR), which expresses the ratio of amplitudes of laterally corresponding electrode pairs. The feature was extracted after expanding the training set using signal augmentation techniques and tested with several machine learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). Most of the ML algorithms showed 100% accuracy with 14 electrodes, and according to our results, perfect accuracy can also be achieved using fewer electrodes. However, AF3, AF4, F7, and F8 electrode combination with kNN classifier which yielded 99.0±0.8% testing accuracy is the best for person identification to maintain both user-friendliness and performance. Surprisingly, the relaxation phase manifested the highest accuracy of the three phases.
Journal Article
Role of forensic odontology in the identification of victims of major mass disasters across the world: A systematic review
Forensic odontology (FO) is regarded in the literature as one of the most reliable and economical scientific methods for victim identification in mass disasters (MDs). The present paper systematically reviews the role of forensic odontologists in various global MDs.
A comprehensive search of the literature databases (PubMed, Medline, SCOPUS, Web of Science and Google Scholar), along with cross-referencing published peer-reviewed articles, was conducted. The search included full texts, abstracts or titles, had no inclusion year limit (searched until September 2017) and was limited to the English language. Keywords included a combination of 'Forensic odontology', 'Dental records', 'Victim identification', 'Natural mass disaster', 'Criminal mass disaster', 'Accidental mass disaster' and 'Victim disaster'.
Of the included disasters (20), 12 (57.14%) were accidental, 5 (23.80%) natural and 3 (19.04%) were criminal. The maximum number of victims was associated with the Japan tsunami (15892), followed by the Thailand tsunami (4280) and the Estonia ferry disaster (852). A total of 23654 victims were reported, of which 20569 (86.96%) were positively identified. Reports from 17 MDs included the use of FO in victim identification [3025 (14.70%) cases]. In addition, 1094 victims (5.31%; from 7 papers) were identified using FO in combination with other methodologies. The highest percentage of victims was identified using FO following the Kentucky air crash (47; 100%), followed by the Newark air crash (38; 76%), the Nepal air crash (10; 71.42%), the France air crash (56; 65.88%), the Australian bushfire (14; 63.63%), and the Estonia ferry disaster (57; 60.63%).
FO has played a significant role in victim identification in several MDs around the world. The success of FO-based identification is heavily dependent on the availability of ante-mortem records from general dental practitioners. Hence, adequate knowledge about FO and appropriate dental record keeping among general dental practitioners are critical.
Journal Article
Can post-capture photographic identification as a wildlife marking technique be undermined by observer error? A case study using King Cobras in northeast Thailand
Identifying individuals with natural markings is increasing in popularity to non-invasively support population studies. However, applying natural variation among individuals requires careful evaluation among target species, snakes for example have little validation of such methods. Here we introduce a mark-free identification method for King Cobras ( Ophiophagus hannah ) from the Sakaerat Biosphere Reserve, in northeast Thailand using both subcaudal scale pholidosis (scale arrangement and number) and unique ventral body markings to distinguish individuals. This project aims to evaluate the impact of observer error on individual identification. Observers of varying expertise, will distinguish between King Cobra individuals using identifying photographs from a previous study. We will ask randomly assigned observers to distinguish individuals via: 1) subcaudal pholidosis, 2) ventral body markings, and 3) combination of both measures. Using Bayesian logistic regression, we will assess the probability observers correctly distinguish individuals. Based on exploratory observations, we hypothesise that there will be a high probability of correct identifications using subcaudal pholidosis and ventral body markings. We aim to stimulate other studies implementing identification techniques for scrutinous assessment of such methods, in order to avoid subsequent errors during long-term population studies.
Journal Article
Using a Convolutional Siamese Network for Image-Based Plant Species Identification with Small Datasets
The application of deep learning techniques may prove difficult when datasets are small. Recently, techniques such as one-shot learning, few-shot learning, and Siamese networks have been proposed to address this problem. In this paper, we propose the use a convolutional Siamese network (CSN) that learns a similarity metric that discriminates between plant species based on images of leaves. Once the CSN has learned the similarity function, its discriminatory power is generalized to classify not just new pictures of the species used during training but also entirely new species for which only a few images are available. This is achieved by exposing the network to pairs of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. We conducted experiments to study two different scenarios. In the first one, the CSN was trained and validated with datasets that comprise 5, 10, 15, 20, 25, and 30 pictures per species, extracted from the well-known Flavia dataset. Then, the trained model was tested with another dataset composed of 320 images (10 images per species) also from Flavia. The obtained accuracy was compared with the results of feeding the same training, validation, and testing datasets to a convolutional neural network (CNN) in order to determine if there is a threshold value t for dataset size that defines the intervals for which either the CSN or the CNN has better accuracy. In the second studied scenario, the accuracy of both the CSN and the CNN—both trained and validated with the same datasets extracted from Flavia—were compared when tested on a set of images of leaves of 20 Costa Rican tree species that are not represented in Flavia.
Journal Article
Blinking characterization from high speed video records. Application to biometric authentication
The evaluation of eye blinking has been used for the diagnosis of neurological disorders and fatigue. Despite the extensive literature, no objective method has been found to analyze its kinematic and dynamic behavior. A non-contact technique based on the high-speed recording of the light reflected by the eyelid in the blinking process and the off-line processing of the sequence is presented. It allows for objectively determining the start and end of a blink, besides obtaining different physical magnitudes: position, speed, eyelid acceleration as well as the power, work and mechanical impulse developed by the muscles involved in the physiological process. The parameters derived from these magnitudes provide a unique set of features that can be used to biometric authentication. This possibility has been tested with a limited number of subjects with a correct identification rate of up to 99.7%, thus showing the potential application of the method.
Journal Article
Coming Paradigm Shift in Forensic Identification Science
Converging legal and scientific forces are pushing the traditional forensic identification sciences toward fundamental change. The assumption of discernible uniqueness that resides at the core of these fields is weakened by evidence of errors in proficiency testing and in actual cases. Changes in the law pertaining to the admissibility of expert evidence in court, together with the emergence of DNA typing as a model for a scientifically defensible approach to questions of shared identity, are driving the older forensic sciences toward a new scientific paradigm.
Journal Article
Assessing cyber‐physical systems to balance maintenance replacement policies and optimise long‐run average costs for aircraft assets
Many aircraft assets are subject to both preventive (scheduled) and corrective (unscheduled) replacement policies to ensure adequate levels of reliability and availability. The problem, particularly for assets that exist in large quantities, is that preventive replacement tasks often involve removing the entire population of assets from the aircraft, regardless of whether any assets were previously replaced on a corrective basis beforehand. To avoid the costs associated with premature asset removal, this study assesses the use of a cyber‐physical systems approach to the management of identified aircraft assets. This approach builds on an industrial architecture that has been implemented and deployed in the aviation maintenance environment. This study outlines how the cyber‐physical based identification of assets can facilitate balancing maintenance replacement policies to optimise long‐run average costs per unit time. A mathematical model is proposed, and the suggested approach is validated using industrial data.
Journal Article