On Using Gait in Forensic Biometrics

Bouchrika, Imed; Goffredo, Michela; Carter, John; Nixon, Mark S.

doi:10.1111/j.1556-4029.2011.01793.x

Cited by 212 publications

(115 citation statements)

References 10 publications

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“…Nowadays optical flow estimation methods have achieved a remarkable level of reliability and precision [9,10,11]. Among them, DeepFlow 2 Image is taken from: http://physics.tutorvista.com/motion/tangential-acceleration.html [12] is a recent technique with excellent performance for large displacement estimation and non-rigid matching. In this paper, the components of resultant acceleration on !-axis !…”

Section: Detecting Acceleration In Computer Imagesmentioning

confidence: 99%

“…Since it is not affected by the low quality of images, gait is the most reliable biometric in the criminal investigation when other biometrics are not available. It has been demonstrated previously that gait can be used in criminal investigations either as body measurements [1] or gait measurements [2]. Figure 1 is a CCTV footage of an Australian jewellery shop murder: the target covered his face during the crime and he was recognised by his gait after it was found in the surveillance video that he had come to the jewellery shop earlier that day.…”

Section: Introductionmentioning

confidence: 99%

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Detecting acceleration for gait and crime scene analysis

Sun¹,

Hare²,

Nixon³

2016

7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016)

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Identifying criminals from CCTV footage is often a difficult task for crime investigations. The quality of CCTV is often low and criminals can cover their face and wear gloves (to withhold fingerprints) when committing a crime. Gait is the optimal choice in this circumstance since people can be recognised by their walking style, even at a distance with low resolution imagery. The location of the frame when the heel strikes the floor is essential for some gait analyses. We propose a new method to detect heel strikes: by radial acceleration which can also generalise to crime analysis. The frame and position of the heel strikes can be estimated by the quantity and the circle centres of radial acceleration, derived from the optical flow (using DeepFlow). Experimental results show high detection rate on two different gait databases and good robustness under different kinds of noise. We analyse detection of heel strikes to show robustness then we analyse crime scenes to show generalisation capability since violent crime often involves much acceleration. As such, we provide a new basis to a baseline technique in crime scene analysis.

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Section: Detecting Acceleration In Computer Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting acceleration for gait and crime scene analysis

Sun¹,

Hare²,

Nixon³

2016

7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016)

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“…CCTV footage or images containing face information are often released to the public for the identification of the perpetrators. For example, in June 2014, British Transport Police (BTP) released CCTV in hunt for angle-grinder gang, who broke the ticket machines at railway stations in UK, as shown [57], left: the perpetrator, right: the suspect; (b) CCTV images for the burglary case in UK [34], left: the perpetrator, right: the suspect.…”

Section: Invited Paper: People Identification and Tracking Through Fumentioning

confidence: 99%

People Identification and Tracking Through Fusion of Facial and Gait Features

Wei

et al. 2014

Biometric Authentication

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Abstract. 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.

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“…Gait analysis finds interest in video surveillance systems [3,4] and forensics science [5,6]. Furthermore, many applications analyze the gait in order to discover pathologies of the body movement [7], rehabilitation therapy [8], identify the fall risk in elderly population in order to assess the frailty syndrome [9,10].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…The latter cannot be always guaranteed, for instance in a video surveillance context. In this case, it is useful to recognize people through biometric parameters that can be captured at a distance and without the collaboration of the person, such as gait [2].Gait analysis finds interest in video surveillance systems [3,4] and forensics science [5,6]. Furthermore, many applications analyze the gait in order to discover pathologies of the body movement [7], rehabilitation therapy [8], identify the fall risk in elderly population in order to assess the frailty syndrome [9,10].…”

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confidence: 99%

Human Classification Using Gait Features

Gianaria

Grangetto

Lucenteforte

et al. 2014

Biometric Authentication

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Abstract. Gait exhibits several advantages with respect to other biometrics features: acquisition can be performed through cheap technology, at a distance and without people collaboration. In this paper we perform gait analysis using skeletal data provided by the Microsoft Kinect sensor. We defined a rich set of physical and behavioral features aiming at identifying the more relevant parameters for gait description. Using SVM we showed that a limited set of behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.Keywords: Gait characterization, Gait analysis, Kinect, Support Vector Machine Introduction and Related WorkBiometrics is the science that studies the human characteristics for anthropometry research, people identification, access control and many more. Biometric features are measurable data classified as physical or behavioral [1]. The former are related to the body and its shape. Some examples are face, hand, iris, retina and fingerprint. Behavioral characteristics are associated to particular human action, for instance handwriting and walking. Automatic recognition systems are often expensive, intrusive and require the cooperation of the subject during the acquisition. The latter cannot be always guaranteed, for instance in a video surveillance context. In this case, it is useful to recognize people through biometric parameters that can be captured at a distance and without the collaboration of the person, such as gait [2].Gait analysis finds interest in video surveillance systems [3,4] and forensics science [5,6]. Furthermore, many applications analyze the gait in order to discover pathologies of the body movement [7], rehabilitation therapy [8], identify the fall risk in elderly population in order to assess the frailty syndrome [9,10]. All these applications are based on the analysis of video and 2D images. Images and videos are processed in order to collect gait parameters applying both model-based approaches, using the definition of a 3D model of the body in movement [11][12][13], or by model-free approaches, that process the silhouette of a walking person [14]. In this paper we implemented a model-based approach using the Microsoft Kinect sensor. Kinect is more that a simple RGB camera, since it is equipped with a depth sensor providing 3D information related to the movements of body joints. The 3D data are more precise compared to the 2D information extracted from images but, on the contrary, the depth sensor, based on the infrared rays, does not work in outside environment and the depth range is quite limited. In literature exists some applications that exploit Kinect 3D data for people recognition and classification. Preis et al. [15] used only anthropometric features, such as height, length of limbs, stride length and speed, for gait characterization. They te...

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On Using Gait in Forensic Biometrics

Cited by 212 publications

References 10 publications

Detecting acceleration for gait and crime scene analysis

Detecting acceleration for gait and crime scene analysis

People Identification and Tracking Through Fusion of Facial and Gait Features

Human Classification Using Gait Features

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