175 research outputs found
Gait Recognition: Databases, Representations, and Applications
There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition
The fundamentals of unimodal palmprint authentication based on a biometric system: A review
Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases
Recommended from our members
Low-resolution human detection and gait recognition in natural scenes
Fast and stable detection of humans in natural scenes is a challenging task due to the varying appearance of the target and diverse background. Under these circumstances a higher-level analysis, e.g. action classification, becomes even more difficult, as it is dependent on the quality of the preceding steps in the processing pipeline. In this paper we address this issue on both levels: low-level detection, and high-level classification. Detecting human figures is formulated as a problem of finding maxima of the distribution generated in the Haar cascade's response space. To find these maxima, we employ an augmented Mean Shift algorithm which assigns each hypothesis a confidence measure related to the distance of the classifier's response from the decision boundary. This confidence is incorporated in the Mean Shift formula to direct the search towards the more reliable hypotheses, which ensures a more accurate detection. On top of the detection framework we have developed a Hidden Markov Model action classifier. It is based on a discrete set of key poses obtained via clustering and utilises a simple motion feature representation. Our approach is exemplified by gait recognition. The model has been trained to recognise four different types, separately in the left and right direction, using the video sequences obtained with a stationary DV camcorder. Good performance on the unseen sequences has been observed which validates our approach and suggests it could be adopted for more general action recognition
Person Re-Identification in Distributed Wide-Area Surveillance
Person re-identification (Re-ID) is a fundamental task in automated video surveillance and has been an area of intense research in the past few years. Given an image or video of a person taken from one camera, re-identification is the process of identifying the person from images or videos taken from a different camera. Re-ID is indispensable in establishing consistent labeling across multiple cameras or even within the same camera to re-establish disconnected or lost tracks. Apart from surveillance it has applications in robotics, multimedia, and forensics. Person re-identification is a diffcult problem because of the visual ambiguity and spatio-temporal uncertainty in a person's appearance across different cameras. However, the problem has received significant attention from the computer-vision-research community due to its wide applicability and utility. In this work, we explore the problem of person re-identification for multi-camera tracking, to understand the nature of Re-ID, constraints and conditions under which it is to be addressed and possible solutions to each aspect. We show that Re-ID for multi-camera tracking is inherently an open set Re-ID problem with dynamically evolving gallery and open probe set. We propose multi-feature person models for both single and multi-shot Re-ID with a focus on incorporating unique features suitable for short as well as long period Re-ID. Finally, we adapt a novelty detection technique to address the problem of open set Re-ID. In conclusion we identify the open issues in Re-ID like, long-period Re-ID and scalability along with a discussion on potential directions for further research.Computer Science, Department o
Re-identification and semantic retrieval of pedestrians in video surveillance scenarios
Person re-identification consists of recognizing individuals across different sensors of a camera
network. Whereas clothing appearance cues are widely used, other modalities could
be exploited as additional information sources, like anthropometric measures and gait. In
this work we investigate whether the re-identification accuracy of clothing appearance descriptors
can be improved by fusing them with anthropometric measures extracted from
depth data, using RGB-Dsensors, in unconstrained settings. We also propose a dissimilaritybased
framework for building and fusing multi-modal descriptors of pedestrian images for
re-identification tasks, as an alternative to the widely used score-level fusion. The experimental
evaluation is carried out on two data sets including RGB-D data, one of which is a
novel, publicly available data set that we acquired using Kinect sensors.
In this dissertation we also consider a related task, named semantic retrieval of pedestrians
in video surveillance scenarios, which consists of searching images of individuals using
a textual description of clothing appearance as a query, given by a Boolean combination of
predefined attributes. This can be useful in applications like forensic video analysis, where
the query can be obtained froma eyewitness report. We propose a general method for implementing
semantic retrieval as an extension of a given re-identification system that uses any
multiple part-multiple component appearance descriptor. Additionally, we investigate on
deep learning techniques to improve both the accuracy of attribute detectors and generalization
capabilities. Finally, we experimentally evaluate our methods on several benchmark
datasets originally built for re-identification task
Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking
Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática
Human identification from video using advanced gait recognition techniques
The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed
- …