Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers

Agricultural Research Division 107th Annual Report 1993

It is a pleasure to provide the 107th Annual Report of the UNL Agricultural Research Division (ARD). This report contains lists of current faculty; active research projects; refereed journal articles, books, and book chapters published; theses and dissertations written; germplasm/cultivars released; and awards received by faculty. Also included are brief descriptions of accomplishments from selected research projects and the financial report for the period July 1, 1992, to June 30,1993. This report was compiled in compliance with the intent of the law of the State of Nebraska that established the Nebraska Agricultural Experiment Station on March 31, 1887. Foreword ... 5 Administration ... 6 … Administrative Personnel ... 6 … Organizational Chart ... 7 … Administrative Units ... 8 … IANR Research Facilities ... 9 Research Highlights ... 10 Faculty ... 18 … Agricultural/Natural Resources Departments ... 18 … College of Human Resources and Family Sciences ... 23 … Off-Campus Research Centers ... 24 Faculty Awards and Recognition ... 26 Research Projects ... 28 Publications ... 40 Variety and Germplasm Releases ... 64 Research Expenditures ... 6

Robust thermal face recognition using region classifiers

This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 different region classifiers. The region classifiers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classifier is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identification process difficult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature