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.

Document Type

Journal Article

Date of Publication

2016

Publisher

Science Publications

School

School of Engineering

RAS ID

22665

Funders

Ministry of Higher Education & Scientific Research (Iraq)

Edith Cowan University

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Comments

Razzaq, A., Hussain, Z., Mohammed, H. (2016). Structural geodesic-Tchebychev transform: An Image similarity measure for face recognition. Journal of Computer Sciences. 12(9), 464-470.

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

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Link to publisher version (DOI)

10.3844/jcssp.2016.464.470