• Title, Summary, Keyword: Gait recognition

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Kernel Fisher Discriminant Analysis for Natural Gait Cycle Based Gait Recognition

  • Huang, Jun;Wang, Xiuhui;Wang, Jun
    • Journal of Information Processing Systems
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    • v.15 no.4
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    • pp.957-966
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    • 2019
  • This paper studies a novel approach to natural gait cycles based gait recognition via kernel Fisher discriminant analysis (KFDA), which can effectively calculate the features from gait sequences and accelerate the recognition process. The proposed approach firstly extracts the gait silhouettes through moving object detection and segmentation from each gait videos. Secondly, gait energy images (GEIs) are calculated for each gait videos, and used as gait features. Thirdly, KFDA method is used to refine the extracted gait features, and low-dimensional feature vectors for each gait videos can be got. The last is the nearest neighbor classifier is applied to classify. The proposed method is evaluated on the CASIA and USF gait databases, and the results show that our proposed algorithm can get better recognition effect than other existing algorithms.

Walking Features Detection for Human Recognition

  • Viet, Nguyen Anh;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.11 no.6
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    • pp.787-795
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    • 2008
  • Human recognition on camera is an interesting topic in computer vision. While fingerprint and face recognition have been become common, gait is considered as a new biometric feature for distance recognition. In this paper, we propose a gait recognition algorithm based on the knee angle, 2 feet distance, walking velocity and head direction of a person who appear in camera view on one gait cycle. The background subtraction method firstly use for binary moving object extraction and then base on it we continue detect the leg region, head region and get gait features (leg angle, leg swing amplitude). Another feature, walking speed, also can be detected after a gait cycle finished. And then, we compute the errors between calculated features and stored features for recognition. This method gives good results when we performed testing using indoor and outdoor landscape in both lateral, oblique view.

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Transformation Based Walking Speed Normalization for Gait Recognition

  • Kovac, Jure;Peer, Peter
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.11
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    • pp.2690-2701
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    • 2013
  • Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometric can be captured at public places from a distance without subject's collaboration, awareness or even consent. Although current approaches give encouraging results, we are still far from effective use in practical applications. In general, methods set various constraints to circumvent the influence factors like changes of view, walking speed, capture environment, clothing, footwear, object carrying, that have negative impact on recognition results. In this paper we investigate the influence of walking speed variation to different visual based gait recognition approaches and propose normalization based on geometric transformations, which mitigates its influence on recognition results. With the evaluation on MoBo gait dataset we demonstrate the benefits of using such normalization in combination with different types of gait recognition approaches.

Sensitivity Analysis of Width Representation for Gait Recognition

  • Hong, Sungjun;Kim, Euntai
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.16 no.2
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    • pp.87-94
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    • 2016
  • In this paper, we discuss a gait representation based on the width of silhouette in terms of discriminative power and robustness against the noise in silhouette image for gait recognition. Its sensitivity to the noise in silhouette image are rigorously analyzed using probabilistic noisy silhouette model. In addition, we develop a gait recognition system using width representation and identify subjects using the decision level fusion based on majority voting. Experiments on CASIA gait dataset A and the SOTON gait database demonstrate the recognition performance with respect to the noise level added to the silhouette image.

Person Recognition Using Gait and Face Features on Thermal Images (열 영상에서의 걸음걸이와 얼굴 특징을 이용한 개인 인식)

  • Kim, Sa-Mun;Lee, Dae-Jong;Lee, Ho-Hyun;Chun, Myung-Geun
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.65 no.2
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    • pp.130-135
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    • 2016
  • Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.

Gait Recognition Algorithm Based on Feature Fusion of GEI Dynamic Region and Gabor Wavelets

  • Huang, Jun;Wang, Xiuhui;Wang, Jun
    • Journal of Information Processing Systems
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    • v.14 no.4
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    • pp.892-903
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    • 2018
  • The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.

Feasibility Study of Gait Recognition Using Points in Three-Dimensional Space

  • Kim, Minsung;Kim, Mingon;Park, Sumin;Kwon, Junghoon;Park, Jaeheung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.2
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    • pp.124-132
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    • 2013
  • This study investigated the feasibility of gait recognition using points on the body in three-dimensional (3D) space based on comparisons of four different feature vectors. To obtain the point trajectories on the body in 3D, gait motion data were captured from 10 participants using a 3D motion capture system, and four shoes with different heel heights were used to study the effects of heel height on gait recognition. Finally, the recognition rates were compared using four methods and different heel heights.

Secured Authentication through Integration of Gait and Footprint for Human Identification

  • Murukesh, C.;Thanushkodi, K.;Padmanabhan, Preethi;Feroze, Naina Mohamed D.
    • Journal of Electrical Engineering and Technology
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    • v.9 no.6
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    • pp.2118-2125
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    • 2014
  • Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

Gait Recognition and Person Identification for Surveillance Robots (걸음걸이 인식을 통한 감시용 로봇에서의 개인 확인)

  • Park, Jin-Il;Lee, Wook-Jae;Cho, Jae-Hoon;Song, Chang-Kyu;Chun, Myung-Geun
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.5
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    • pp.511-518
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    • 2009
  • The surveillance robot has been an important component in the field of service robot industry. In the surveillance robot technology, one of the most important technology is to identify a person. In this paper, we propose a gait recognition method based on contourlet and fuzzy LDA (Linear Discriminant Analysis) for surveillance robots. After decomposing a gait image into directional subband images by contourlet, features are obtained in each subband by the fuzzy LDA. The final gait recognition is performed by a fusion technique that effectively combines similarities calculated respectively in each local subband. To show the effectiveness of the proposed algorithm, various experiments are performed for CBNU and NLPR DB datasets. From these, we obtained better classification rates in comparison with the result produced by previous methods.

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

  • Hoang, Thang;Nguyen, Thuc;Luong, Chuyen;Do, Son;Choi, Deokjai
    • Journal of Information Processing Systems
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    • v.9 no.2
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    • pp.333-348
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    • 2013
  • Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately $91.33{\pm}0.67%$ for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.