Gait recognition: a challenging signal processing technology for biometric identification

Boulgouris, Nikolaos V.; Hatzinakos, Dimitrios; Plataniotis, Konstantinos N.

doi:10.1109/msp.2005.1550191

Cited by 317 publications

(157 citation statements)

References 41 publications

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“…After getting the intersecting point, the two mean values of an intersecting point of an individual subject are computed shown in (6). There are many types of membership function choose one of them dependent on the application.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Many authors can worked on silhouettes images and Gait Energy images [5], [6], [7]. Haiping Lu et al [8], [9] defined the body part 22 human parameter.…”

Section: Related Workmentioning

confidence: 99%

“…Walking pattern can be analyzed by a gait cycle. Gait cycle is repeated motion of body part [6], [9]. The style of walking or gait cycle of every person is unique [5], [8].…”

Section: A Human Gaitmentioning

confidence: 99%

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Gait Recognition with Fuzzy Classification Using Shoulder Body Joint

Bharti¹,

Gupta²

2013

IJFCC

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Abstract-In this paper, the adopted methodology based on intersection with fuzzy classification. Consider two human body joint i.e. shoulder and feet. Feet body joint is separated in to two component toe and heel of the left and right leg. Two triangles are formed on joining the shoulder and feet (toe and heel). The intersecting points are computed on CASIA database. Fuzzy classification yield enhanced recognition rate.

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Section: Proposed Methodologymentioning

confidence: 99%

“…Many authors can worked on silhouettes images and Gait Energy images [5], [6], [7]. Haiping Lu et al [8], [9] defined the body part 22 human parameter.…”

Section: Related Workmentioning

confidence: 99%

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Gait Recognition with Fuzzy Classification Using Shoulder Body Joint

Bharti¹,

Gupta²

2013

IJFCC

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“…However, templates are weak against the natural variations of individual behavioral patterns since they only encode an average representation of observed samples [19]. Statistical models such as Gaussian mixture models (GMM) and hidden Markov models (HMM) have successfully handled variations in individual behavioral patterns [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A statistical approach for person verification using human behavioral patterns

et al. 2013

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We propose a person verification method using behavioral patterns of human upper body motion. Behavioral patterns are represented by three-dimensional features obtained from a time-of-flight camera. We take a statistical approach to model the behavioral patterns using Gaussian mixture models (GMM) and support vector machines. We employ the maximum likelihood linear regression adaptation method to estimate GMM parameters with a limited amount of data. Experimental results show that it reduced by 28.6% the relative equal error rates from a system using the maximum likelihood estimation with 25 samples per subject. We also demonstrate that the proposed approach is robust against variations in body motion over time.

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“…Gait analysis techniques based on the global geometry of the objects have been discussed by BOULGOURIS et al [6]. According to them, techniques that employ binary images are believed to be particularly suited for most practical applications since color or texture informations might not be available or appropriate.…”

Section: Introductionmentioning

confidence: 99%

Robust Analysis of Silhouettes by Morphological Size Distributions

Barnich

Jodogne

Droogenbroeck

2006

Advanced Concepts for Intelligent Vision Systems

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Abstract. We address the topic of real-time analysis and recognition of silhouettes. The method that we propose first produces object features obtained by a new type of morphological operators, which can be seen as an extension of existing granulometric filters, and then insert them into a tailored classification scheme. Intuitively, given a binary segmented image, our operator produces the set of all the largest rectangles that can be wedged inside any connected component of the image. The later are obtained by a standard background subtraction technique and morphological filtering. To classify connected components into one of the known object categories, the rectangles of a connected component are submitted to a machine learning algorithm called EXtremely RAndomized trees (Extra-trees). The machine learning algorithm is fed with a static database of silhouettes that contains both positive and negative instances. The whole process, including image processing and rectangle classification, is carried out in real-time. Finally we evaluate our approach on one of today's hot topic: the detection of human silhouettes. We discuss experimental results and show that our method is stable and computationally effective. Therefore, we assess that algorithms like ours introduce new ways for the detection of human in video sequences.

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Gait recognition: a challenging signal processing technology for biometric identification

Cited by 317 publications

References 41 publications

Gait Recognition with Fuzzy Classification Using Shoulder Body Joint

Gait Recognition with Fuzzy Classification Using Shoulder Body Joint

A statistical approach for person verification using human behavioral patterns

Robust Analysis of Silhouettes by Morphological Size Distributions

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