Affiliation:
1. UNC-Charlotte, USA
2. University of North Carolina, Charlotte, USA
Abstract
Over the past decade Facial Recognition has become more cohesive and reliable than ever before. We begin with an analysis explaining why certain facial recognition methodologies examined under FERET, FRVT 2000, FRVT 2002, and FRVT 2006 have become stronger and why other approaches to facial recognition are losing traction. Second, we cluster the stronger approaches in terms of what approaches are mutually inclusive or exclusive to surrounding methodologies. Third, we discuss and compare emerging facial recognition technology in light of the aforementioned clusters. In conclusion, we suggest a road map that takes into consideration the final goals of each cluster, that given each clusters weakness, will make it easier to combine methodologies with surrounding clusters.
Reference12 articles.
1. Delac, K., & Grgic, M. (2007). Face Recognition, I-Tech Education and Publishing, Vienna, 558 pages.
2. Deniz, O., Castrillon, M., & Hernández, M. (2001). Face recognition using independent component analysis and support vector machines, Proceedings of Audio- and Video-Based Biometric Person Authentication: Third International Conference, LNCS 2091, Springer.
3. Du, W., Inoue, K., & Urahama, K. (2005). Dimensionality reduction for semi-supervised face recognition, Fuzzy Systems and Knowledge Discovery (FSKD), LNAI 3613-14, Springer, 1-10.
4. Application of the Karhunen-Loeve procedure for the characterization of human faces
5. Lewis, R. A., & Ras, Z. W. (2005). New methodology of facial recognition, Intelligent Information Processing and Web Mining, Advances in Soft Computing, Proceedings of the IIS’2005 Symposium, Gdansk, Poland, Springer, 615-632.