This Research study proposes a novel method for face recognition based on Anthropometric features that make use of an integrated approach comprising of a global and personalized models. The system is aimed to at situations where lighting, illumination, and pose variations cause problems in face recognition. A Personalized model covers the individual aging patterns while a Global model captures general aging patterns in the database. We introduced a de-aging factor that de-ages each individual in the database test and training sets. We used the k nearest neighbor approach for building a personalized model and global model. Regression analysis was applied to build the models. During the test phase, we resort to voting on different features. We used FG-Net database for checking the results of our technique and achieved 65 percent Rank 1 identification rate.

Date Published: 4 March 2015
PDF: 7 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94430A (4 March 2015); doi: 10.1117/12.2179506

Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)