Research Article Open Access

Structural Geodesic-Tchebychev Transform: An Image Similarity Measure for Face Recognition

Ali Nadhim Razzaq1, Zahir M. Hussain2 and Hind Rustum Mohammed1
  • 1 University of Kufa, Iraq
  • 2 ECU, Australia

Abstract

This work presents a new holistic measure for face recognition. Face recognition involves three steps: Face Detection, Feature Extraction and Matching. In the face detection process to identify the face area in face images, Viola-Jones algorithm has been used. Feature extraction is based on performing double-transformation, where discrete Tchebychev transform is performed on the geodesic distance transform of the grayscale image. Structural Similarity (SSIM) is applied to the resulting image double-transform to find matching factor with other image faces in the FEI (Brazilian) database. Performance is measured using a confidence criterion based on the similarity distance between the recognized person (best match) and the next possible ambiguity (second-best match). Simulation results showed that the proposed approach handles the face recognition efficiently as compared with SSIM.

Journal of Computer Science
Volume 12 No. 9, 2016, 464-470

DOI: https://doi.org/10.3844/jcssp.2016.464.470

Submitted On: 4 July 2016 Published On: 29 November 2016

How to Cite: Razzaq, A. N., Hussain, Z. M. & Mohammed, H. R. (2016). Structural Geodesic-Tchebychev Transform: An Image Similarity Measure for Face Recognition. Journal of Computer Science, 12(9), 464-470. https://doi.org/10.3844/jcssp.2016.464.470

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Keywords

  • Discrete Tchebychev Moments
  • Generalized Geodesy via Geodesic Time
  • Structural Similarity (SSIM)
  • Viola-Jones
  • Face Recognition
  • Image Processing