29,746 research outputs found

    Vision-based techniques for gait recognition

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    Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance

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

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    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

    Global panopticon

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    Contemporary panopticon infrastructure and technologies are deployed to secure citizens using video surveillance, biometrics, labelling technologies, satellites and the global fibre network. This is an expanding business sector

    Multi-disciplinary Collaborations in Measurement of Human Motion

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    Comparative Medicine - OneHealth and Comparative Medicine Poster SessionBioengineering is a broad and rapidly-growing discipline defined as the application of engineering principles to biological systems. Although bioengineering is diverse in nature, the study of human movement is common to many bioengineering subdisciplines such as biomechanics and biometrics. Biomechanics is the science that examines the forces acting upon and within a biological structure and effects produced by such forces [1]. Measurement of ground reaction forces, limb motion, and muscle activation are fundamental research components in musculoskeletal biomechanics. Researchers in this field have used these measurements to quantify human gait, balance, and posture in a multitude of applications including age-related fall risk [2-4], muscle fatigue [5-7], and balance-related pathologies such as Parkinson's disease [8-10], and stroke [11, 12]. Additionally, these measurements play a vital role in computational biomechanics models. For example, the inverse dynamics method incorporates measured ground reaction forces and body motions to calculate the net reaction forces and torques acting on body joints [13]. Biometrics is the science of confirming or discovering individuals' identities based on their specific biological or behavioral traits [14]. Gait is one such modality which can be used for biometric identification. It is based on the uniqueness of an individual's locomotion patterns [15]. In addition, we are interested in high-speed video analyses of micro-saccades and blink reflexes for spoof-proofing of biometric identification systems, biometric identification, and psychometry. We have shown that startle blink intensity can be derived from high- speed video [18], enabling video-based psychophysiological biometrics for detection of subject-specific affective-cognitive information [19]. The Human Motion Laboratory at the University of Missouri - Kansas City is dedicated to measuring the characteristics of human motion. The lab includes a VICON MX 6-camera motion capture system, 4 AMTI OR6-6 force platforms, and a Delsys Myomonitor IV 16-channel wireless EMG system. This equipment represents an experimental infrastructure mutually supporting the biomechanics and biometrics research efforts of four research labs. The scope of these research efforts includes aging, affective computing, psychophysiological biometrics, orthopedics, and human dynamics pathology. The lab capitalizes on a synergistic environment for characterization and measurement of human movement and the interrelated nature of the research activities. The four main research areas that the Human Motion Laboratory supports are: •Computational Biomechanics •Biometrics of Human Motion •Experimental Biomechanics •Body Area Sensor Network

    On Using Gait in Forensic Biometrics

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    Given the continuing advances in gait biometrics, it appears prudent to investigate the translation of these techniques for forensic use. We address the question as to the confidence that might be given between any two such measurements. We use the locations of ankle, knee and hip to derive a measure of the match between walking subjects in image sequences. The Instantaneous Posture Match algorithm, using Harr templates, kinematics and anthropomorphic knowledge is used to determine their location. This is demonstrated using real CCTV recorded at Gatwick Airport, laboratory images from the multi-view CASIA-B dataset and an example of real scene of crime video. To access the measurement confidence we study the mean intra- and inter-match scores as a function of database size. These measures converge to constant and separate values, indicating that the match measure derived from individual comparisons is considerably smaller than the average match measure from a population

    On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data

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    In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here

    CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

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    With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%
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