Effective part-based gait identification using frequency-domain gait entropy features

Rokanujjaman, Md.; Islam, Md. Shariful; Hossain, Md. Altab; Islam, Md. Rezaul; Makihara, Yasushi; Yagi, Yasushi

doi:10.1007/s11042-013-1770-8

Cited by 45 publications

(25 citation statements)

References 28 publications

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“…The spatial metric learning-based approaches concentrate on learning a more discriminant feature space from original appearance-based features to achieve better performance against the covariates. Additionally, there are two further categories within the spatial metric learning-based approaches: whole-based [11][12][13][24][25][26][27] and part-based approaches [28][29][30][31]. For the whole-based approaches, the holistic appearance-based features are projected into a discriminative space to make them more robust against the covariate conditions.…”

Section: Spatial Metric Learning-based Approaches To Gait Recognitionmentioning

confidence: 99%

“…Weights were based on the similarity between extracted features and those in the database for standard clothing. Rokanujjaman et al [29] defined more effective and less effective body parts by analyzing cumulative row-wise recognition rates. The frequency domain-based gait entropy features (EnDFT) of the more effective parts were used for recognition.…”

Section: Spatial Metric Learning-based Approaches To Gait Recognitionmentioning

confidence: 99%

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Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status

Makihara

et al. 2018

Applied Sciences

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Silhouette-based gait representations are widely used in the current gait recognition community due to their effectiveness and efficiency, but they are subject to changes in covariate conditions such as clothing and carrying status. Therefore, we propose a gait energy response function (GERF) that transforms a gait energy (i.e., an intensity value) of a silhouette-based gait feature into a value more suitable for handling these covariate conditions. Additionally, since the discrimination capability of gait energies, as well as the degree to which they are affected by the covariate conditions, differs among body parts, we extend the GERF framework to spatially dependent GERF (SD-GERF) which accounts for spatial dependence. Moreover, the proposed GERFs are represented as a vector in the transformation lookup table and are optimized through an efficient generalized eigenvalue problem in a closed form. Finally, two post-processing techniques, Gabor filtering and spatial metric learning, are employed for the transformed gait features to boost the accuracy. Experimental results with three publicly available datasets including clothing and carrying status variations show the state-of-the-art performance of the proposed method compared with other state-of-the-art methods.

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Section: Spatial Metric Learning-based Approaches To Gait Recognitionmentioning

confidence: 99%

Section: Spatial Metric Learning-based Approaches To Gait Recognitionmentioning

confidence: 99%

Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status

Makihara

et al. 2018

Applied Sciences

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“…Frequency domain features using Discrete Fourier Transform (DFT) operator on gait images have yielded good results in (Makihara et al, 2006). Discrete Fourier Transform based Entropy (EnDFT) (Rokanujjaman et al, 2013) considers the frequency domain specific features of gait entropy image (GEnI) whereas Gait flow Image (GFI) (Lam et al, 2011) uses an optical flow method to figure out the relative motion information in a gait sequence. By computing the average of the optical flow information, GFI proved to perform better as compared to GEI on USF (University of South Florida) gait dataset (Sarkar et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Gait based authentication using gait information image features

Arora

Hanmandlu

Srivastava

2015

Pattern Recognition Letters

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“…And Hossain et al [Hossain, Makihara, Wang et al (2010)] proposed a part-based gait identification in the light of substantial clothing variations, which exploits the discrimination capability as a matching weight for each part and controls the weights adaptively based on the distribution of distances between the probe and all the galleries. Rokanujjaman et al [Rokanujjaman, Islam, Hossain et al (2015)] proposed an effective parts definition approach based on the contribution of each row when it merges orderly from bottom to top. It shows that some rows have positive effects and some rows have negative effects for gait recognition.…”

Section: Introductionmentioning

confidence: 99%

Attention-Aware Network With Latent Semantic Analysis for Clothing Invariant Gait Recognition

Ling¹,

Wu²,

Li³

et al. 2019

Computers, Materials &Amp; Continua

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Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from the raw input. Thus, we propose a new CNN-based method which leverages advantage of the latent semantic analysis and attention mechanism. Based on discriminative features extracted using attention and the latent semantic analysis module respectively, multi-modal fusion method is proposed to fuse those features for its high fault tolerance in the decision level. Experiments on the most challenging clothing variation dataset: OU-ISIR TEADMILL dataset B show that our method outperforms other state-of-art gait approaches.

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Effective part-based gait identification using frequency-domain gait entropy features

Cited by 45 publications

References 28 publications

Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status

Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status

Gait based authentication using gait information image features

Attention-Aware Network With Latent Semantic Analysis for Clothing Invariant Gait Recognition

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