Earticle

초록

영어

Individual identification at a distance using gait features has newly gained growing interest from biometrics researchers. Most of the researchers have been shown that different covariate factors can affect different parts of the human body. In this paper, we propose a new approach that minimizes these difficulties, especially for carrying objects by combining static, dynamic, and part-based features. The Gait Features of the walking sequences are extracted by selecting only four sub bands of the Discrete Wavelet Transform (DWT) of the individual images. Moreover, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are implemented to extract lowest and middle frequency components that are used to create robust gait feature images (RGFIs). Then we select effective parts of the body from the Robust Gait Feature Images. After that, these parts of the body are trained using Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) to identify individuals. Experimental result shows promising performance in comparison with other methods.

목차

Abstract
 1. Introduction
 2. Gait Dataset with Various Covariate Factors
 3. Feature Extraction
  3.1. Pre-processing
  3.2. Gait Period Estimation
  3.3. Robust Gait Feature Image
 4. Identification and Performance Evaluation
  4.1. Feature Matching
  4.2. Experimental Results and Comparison
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• K. M. Ibrahim Khalilullah [ Lecturer, Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka-1216, Bangladesh ]
• Delowar Hossain [ Ph. D Fellow, Intelligent Robotics Lab, Department of Electrical and Electronics System Engineering, Faculty of Engineering, University of Toyama, Japan ]
• Md. Ekramul Hamid [ Professor, Department of Computer Science and Engineering, University of Rajshahi, Rajshahi-6205, Bangladesh ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605