Implementation of Face Recognition Based on 3D Image

Abstract:

Biometric is used to confirm the unique of identity. In general, face is the most characteristic to recognize a person. In this paper, it is emphasized and compared the quality of 2D and 3D face recognition. There are three parts in this paper. First part is the detection of skin color which is used RGB color space. In order to reduce color red and green which are sensitive to illuminant, Normalized Color Coordinate (NCC) method is chosen to pick up the range of skin color directly. Second, to increase choosing of the important characteristics by Principle Component Analysis (PCA) the wavelength distinguishes technique is used to make 3D images. The third part is about identifying. An improved PCA through a transfer matrix to get optimal total scatter matrix of within-class scatter matrix is used. The optimal total scatter matrix represents the eigenvalue of face characteristics. Finally, the recognition rate and process performance between 2D and 3D images are compared via Euclidean Distance. The efficiency and recognition rate of 3D images are superior to 2D images. The recognition rate of 3D images attains to 92% and costs 0.39 second to recognize each image. It is improved 28% compared with the recognition rate of 2D images.

• Periodical: Applied Mechanics and Materials (Volume 311)
• Pages: 173-178
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.311.173
• Online since: February 2013
• Authors: Jia Shing Sheu, Ho Nien Shou, Li Peng Wang, Tsong Liang Huang
• Keywords: 3D Image, Euclidean Distance, Face Recognition, Principal Component Analysis (PCA)

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