Improved Gait Recognition using Gradient Histogram Gaussian Image

被引：12

作者：

Arora, Parul ^{[1
]}

Srivastava, Smriti ^{[1
]}

Arora, Kunal ^{[1
]}

Bareja, Shreya ^{[1
]}

机构：

[1] Netap Subhas Inst Technol, New Delhi, India

来源：

SECOND INTERNATIONAL SYMPOSIUM ON COMPUTER VISION AND THE INTERNET (VISIONNET'15) | 2015年 / 58卷

关键词：

Gait Gaussian Image; Histogram of Oriented Gradients; Gradient Histogram Gaussian Image; Nearest Neighbor;

D O I：

10.1016/j.procs.2015.08.049

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we have proposed the incorporation of HOG (Histogram of Oriented Gradients) to Gait Gaussian Image for visibly improved results in gait recognition. This new spatial-temporal representation is called Gradient Histogram Gaussian Image (GHGI). It is almost similar to Gait Energy Image (GEI) but the usage of Gaussian function and further application of HOG considerably increases efficiency and reduces amalgamation of noise. In GEI, silhouettes are averaged and hence only edge information at the boundaries is preserved. Contrary to this, our concept takes the Gaussian distribution over a cycle and computes gradient histograms at all locations. Edge information inside the person silhouette is also preserved this way. The features derived from GHGI are classified using the Nearest Neighbor classifier. The supporting simulations are performed on OU-ISIR database A and B, commonly referred to as the Treadmill database A and B. The potency of our hypothesis is validated with comparative results. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：408 / 413

页数：6

共 16 条

[1]

[Anonymous], 4 UK COMP VIS STUD W

[2]

Arora P, 2015, 2ND INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INTEGRATED NETWORKS (SPIN) 2015, P791, DOI 10.1109/SPIN.2015.7095388

[3]

ChewYean Yam, 2002, Proceedings of the Fifth Asian Conference on Computer Vision, P1

[4]

Chin-Pan Huang, 2011, Proceedings of the 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC 2011), P353, DOI 10.1109/IMCCC.2011.95

[5] Histograms of oriented gradients for human detection [J].

Dalal, N ;

Triggs, B .

2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS, 2005, :886-893

[6]

Gong S, 2009, P INT C IM CRIM DET, V1, P2

[7] Individual recognition using Gait Energy Image [J].

Han, J ;

Bhanu, B .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006, 28 (02) :316-322

[8]

Hofmann M., 2012, 2012 IEEE Fifth International Conference On Biometrics: Theory, Applications And Systems (BTAS 2012), P399, DOI 10.1109/BTAS.2012.6374606

[9]

HOFMANN M, 2012, 19 IEEE INT C IM PRO, P1389

[10]

Hu Ng, 2011, International Journal of New Computer Architectures and their Applications, V1, P358

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