Measurements of Anthropometric Characteristics of Persons from Video Surveillance

Povijesno različito valorizirana, ali u konačnici odbačena metoda antropološke identifikacije poznata pod imenom Bertillonage, u suvremenim uvjetima mogla bi se, barem na simboličkoj razini, reafirmirati. Naime, Bertillonova metoda, zbog znanstveno-tehnoloških limita, te istodobno nadiruće daktiloskopije, odbačena je i „arhivirana“ kao povijesna znamenitost, gotovo na razini rariteta. U današnjim uvjetima sofisticiranih sigurnosnih računalnih i drugih alata i znatne „pokrivenosti“ javnih prostora različitim oblicima videonadzora, otvara se mogućnost pribavljanja golemih podataka o snimljenim osobama i registriranim događajima, kako u smislu identifikacije tih osoba, tako i u smislu utvrđivanja tijeka i dinamike (okolnosti) događaja. Iako postoje brojni forenzični računalni alati za automatiziranu antropometrijsku identifikaciju, u ovom radu prikazan je dio naših istraživanja o mogućnosti i pouzdanosti mjerenja antropometrijskih karakteristika maskiranih ili na snimci neprepoznatljivih počinitelja kaznenih djela snimljenih sigurnosnim videokamerama, pritom vodeći računa o varijabilnim parametrima poput visine kamere, kuta snimanja, kuta osi objektiva u odnosu na objekt snimanja, udaljenosti objekta od objektiva i dr. te njihovu utjecaju na konačni rezultat mjerenja. Također, u ovom radu prikazat će se relativno jednostavna i pouzdana metoda osiguravanja tzv. nespornog materijala tako da osigurava respektabilnu razinu preciznosti mjerenja visine nepoznate osobe.The method of anthropological identification, known as Bertillonage, has been differently valued throughout history, only to be ultimately rejected. Due to scientific and technological limits and surpassing dactyloscopy, Bertillon’s method was rejected and “archived” as a historical landmark, almost at the level of rarities. However, the method could be reaffirmed in current conditions, at least symbolically. Today’s sophisticated security technology tools and public video surveillance (CCTV) as a more widespread feature are opening an opportunity to collect data about the persons and events recorded, not only the identity of those persons but also to determine the course and circumstances of the event. Although there are numerous forensic software tools for automated anthropometric identification, this paper empirically tests the possibility of measuring anthropometric characteristics of masked or unrecognized perpetrators of crimes recorded by security video cameras, taking into account various parameters such as camera height, angle shooting, the angle of the lens axis in relation to the shooting object, the height of the camera, the distance of the object from the lens, and their impact on the final measurement result. This paper introduces a relatively simple and reliable method of securing the so-called undisputed material to ensure a respectable level of measurement precision

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

What else does your biometric data reveal? A survey on soft biometrics

International audienceRecent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics

On-demand computerised decision support in paediatric emergency medication administration

Medication errors are common and contribute significantly to avoidable morbidity and mortality. These errors are more common in paediatrics than in adult medicine and yet more common in emergencies than in non-urgent scenarios. Emergency medication use in paediatrics is a complex process, often performed by a multidisciplinary team. This practise spans paediatric emergency weight estimation, medication ordering and the preparation, labelling and finally administration of drugs to critically ill children. Discrepancies during any one of these stages may be the cause of a medication error. This thesis describes the feasibility testing and iterative development of On-Demand Computerised Decision Support (ODCDSS), a digital medication safety system designed to support emergency doctors and nurses at all stages of medication use during paediatric resuscitation. This thesis decouples paediatric weight estimation and medication administration, with each being examined separately. First, opportunities to incorporate digitally-supported weight estimation methods into a comprehensive decision support system are explored. This includes efforts to improve well-established but inaccurate methods such as age-based weight estimation, and an exploration into whether emerging technologies such as three- dimensional imaging could bring greater accuracy than current methods. To validate the design rationale behind ODCDSS, simulated resuscitations are examined using a human factors approach. Human Reliability Analysis (HRA) is 2 used to show both that the incidence of medication error is dangerously high and that many errors are clinically significant, as well to identify the individual process steps that contribute to these errors. This thesis is the first to describe the design and development of ODCDSS. The effectiveness of this experimental prototype was tested in a simulated crossover study which determined that using ODCDSS significantly reduced the odds of a clinically significant medication error occurring. The use of ODCDSS, however, was not error free, and HRA was re-applied to determine precisely where the prototype system required design refinement.Open Acces

7th INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ENGINEERING - SIE 2018, PROCEEDINGS

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi

7th INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ENGINEERING - SIE 2018, PROCEEDINGS

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi

Proceedings / 6th International Symposium of Industrial Engineering - SIE 2015, 24th-25th September, 2015, Belgrade

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi

Proceedings / 6th International Symposium of Industrial Engineering - SIE 2015, 24th-25th September, 2015, Belgrade

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi