On gait as a biometric: progress and prospects

There is increasing interest in automatic recognition by gait given its unique capability to recognize people at a distance when other biometrics are obscured. Application domains are those of any noninvasive biometric, but with particular advantage in surveillance scenarios. Its recognition capability is supported by studies in other domains such as medicine (biomechanics), mathematics and psychology which also suggest that gait is unique. Further, examples of recognition by gait can be found in literature, with early reference by Shakespeare concerning recognition by the way people walk. Many of the current approaches confirm the early results that suggested gait could be used for identification, and now on much larger databases. This has been especially influenced by DARPA’s Human ID at a Distance research program with its wide scenario of data and approaches. Gait has benefited from the developments in other biometrics and has led to new insight particularly in view of covariates. Equally, gait-recognition approaches concern extraction and description of moving articulated shapes and this has wider implications than just in biometrics

People identification and tracking through fusion of facial and gait features

This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance

People identification and tracking through fusion of facial and gait features

This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Multi-Modal Biometrics: Applications, Strategies and Operations

The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented

Hierarchical and multi-featured fusion for effective gait recognition under variable scenarios

Human identification by gait analysis has attracted a great deal of interest in the computer vision and forensics communities as an unobtrusive technique that is capable of recognizing humans at range. In recent years, significant progress has been made, and a number of approaches capable of this task have been proposed and developed. Among them, approaches based on single source features are the most popular. However the recognition rate of these methods is often unsatisfactory due to the lack of information contained in single feature sources. Consequently, in this paper, a hierarchal and multi-featured fusion approach is proposed for effective gait recognition. In practice, using more features for fusion does not necessarily mean a better recognition rate and features should in fact be carefully selected such that they are complementary to each other. Here, complementary features are extracted in three groups: Dynamic Region Area; Extension and Space features; and 2D Stick Figure Model features. To balance the proportion of features used in fusion a hierarchical feature-level fusion method is proposed. Comprehensive results of applying the proposed techniques to three well-known datasets have demonstrated that our fusion based approach can improve the overall recognition rate when compared to a benchmark algorithm

Effectiveness of Multi-View Face Images and Anthropometric Data In Real-Time Networked Biometrics

Over the years, biometric systems have evolved into a reliable mechanism for establishing identity of individuals in the context of applications such as access control, personnel screening and criminal identification. However, recent terror attacks, security threats and intrusion attempts have necessitated a transition to modern biometric systems that can identify humans under unconstrained environments, in real-time. Specifically, the following are three critical transitions that are needed and which form the focus of this thesis: (1) In contrast to operation in an offline mode using previously acquired photographs and videos obtained under controlled environments, it is required that identification be performed in a real-time dynamic mode using images that are continuously streaming in, each from a potentially different view (front, profile, partial profile) and with different quality (pose and resolution). (2) While different multi-modal fusion techniques have been developed to improve system accuracy, these techniques have mainly focused on combining the face biometrics with modalities such as iris and fingerprints that are more reliable but require user cooperation for acquisition. In contrast, the challenge in a real-time networked biometric system is that of combining opportunistically captured multi-view facial images along with soft biometric traits such as height, gait, attire and color that do not require user cooperation. (3) Typical operation is expected to be in an open-set mode where the number of subjects that enrolled in the system is much smaller than the number of probe subjects; yet the system is required to generate high accuracy.;To address these challenges and to make a successful transition to real-time human identification systems, this thesis makes the following contributions: (1) A score-based multi- modal, multi-sample fusion technique is designed to combine face images acquired by a multi-camera network and the effectiveness of opportunistically acquired multi-view face images using a camera network in improving the identification performance is characterized; (2) The multi-view face acquisition system is complemented by a network of Microsoft Kinects for extracting human anthropometric features (specifically height, shoulder width and arm length). The score-fusion technique is augmented to utilize human anthropometric data and the effectiveness of this data is characterized. (3) The performance of the system is demonstrated using a database of 51 subjects collected using the networked biometric data acquisition system.;Our results show improved recognition accuracy when face information from multiple views is utilized for recognition and also indicate that a given level of accuracy can be attained with fewer probe images (lesser time) when compared with a uni-modal biometric system

Biometric Based Intrusion Detection System using Dempster-Shafer Theory for Mobile Ad hoc Network Security

In wireless mobile ad hoc network, mainly, two approaches are followed to protect the security such as prevention-based approaches and detection-based approaches. A Mobile Ad hoc Network (MANET) is a collection of autonomous wireless mobile nodes forming temporary network to interchange data (data packets) without using any fixed topology or centralized administration. In this dynamic network, each node changes its geographical position and acts as a router for forwarding packets to the other node. Current MANETs are basically vulnerable to different types of attacks. The multimodal biometric technology gives possible resolves for continuous user authentication and vulnerability in high security mobile ad hoc networks (MANETs). Dempster’s rule for combination gives a numerical method for combining multiple pieces of data from unreliable observers. This paper studies biometric authentication and intrusion detection system with data fusion using Dempster–Shafer theory in such MANETs. Multimodal biometric technologies are arrayed to work with intrusion detection to improve the limitations of unimodal biometric technique