3D Face Fecognition using Fourier-Cosine Transform Coefficients Fusion
Naveen S1, R.S Moni2
1Naveen S, Assistant Professor, Department of ECE, LBS Institute of Technology for Women, Trivandrum (Kerala), India.
2Dr R.S Moni, Professor, Department of ECE, Marian Engineering College, Trivandrum (Kerala), India.
Manuscript received on 15 June 2015 | Revised Manuscript received on 25 June 2015 | Manuscript Published on 30 June 2015 | PP: 50-56 | Volume-4 Issue-5, June 2015 | Retrieval Number: E4006064515/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 3D Face recognition has been an area of interest among researchers for the past few decades especially in pattern recognition. The main advantage of 3D Face recognition is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person identification using 3D Face data. Use of unregistered 3D Face data significantly increases the operational speed of the system with huge database enrolment. In this work, unregistered 3D Face data is fed to a classifier in multiple spectral representations of the same data. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used for the spectral representations. The face recognition accuracy obtained when the feature extractors are used individually is evaluated. Fusion of the matching scores proves that the recognition accuracy can be improved significantly by fusion of scores of multiple representations. FRAV3D database is used for testing the algorithm.
Keywords: Point Cloud, Rotation Invariance, Pose Correction, Depth Map, Spectral Transformations, and Principal Component Analysis.
Scope of the Article: Cloud Computing