Gait recognition based on inverse fast fourier transform Gaussian and enhancement Histogram Oriented of Gradient

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Abstract

Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus, this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of new image representation and the classification. The first phase comprises the image binarization process and energy image generation using average gait image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7%, 93.1% and 99.6%compared to HoG method by 94.1%, 85.9% and 96.2% in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event, and our proposed method outperforms Dalal Method.

Original languageEnglish
Pages (from-to)1402-1410
Number of pages9
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume8
Issue number4-2
Publication statusPublished - 1 Jan 2018

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Fourier Analysis
gait
Gait
Fast Fourier transforms
Feature extraction
Motorcycles
Image processing
Classifiers
methodology
energy
Testing
Research
bags
image analysis
Datasets

Keywords

  • Features
  • Frequency
  • Fusion
  • Gait recognition
  • Histogram
  • HOG
  • IFFT
  • Spatial

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

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title = "Gait recognition based on inverse fast fourier transform Gaussian and enhancement Histogram Oriented of Gradient",
abstract = "Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus, this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of new image representation and the classification. The first phase comprises the image binarization process and energy image generation using average gait image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7{\%}, 93.1{\%} and 99.6{\%}compared to HoG method by 94.1{\%}, 85.9{\%} and 96.2{\%} in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event, and our proposed method outperforms Dalal Method.",
keywords = "Features, Frequency, Fusion, Gait recognition, Histogram, HOG, IFFT, Spatial",
author = "Rahman, {S. Z.A.} and {Sheikh Abdullah}, {Siti Norul Huda} and {Zainol Ariffin }, {Khairul Akram}",
year = "2018",
month = "1",
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language = "English",
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pages = "1402--1410",
journal = "International Journal on Advanced Science, Engineering and Information Technology",
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T1 - Gait recognition based on inverse fast fourier transform Gaussian and enhancement Histogram Oriented of Gradient

AU - Rahman, S. Z.A.

AU - Sheikh Abdullah, Siti Norul Huda

AU - Zainol Ariffin , Khairul Akram

PY - 2018/1/1

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