Title :
Face Recognition Using a Multilinear Framework
Author_Institution :
Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Abstract :
Appearance-based method classifies images by their appearances, using statistical knowledge learned from training data. This paper presents a framework of multilinear algebra-based face recognition, which proposes the use of tensor projector. We will discuss the choice of distance function, which is not usually paid attention to in most research on this area. In fact, a properly chosen distance function is nontrivial to the recognition rate, even for the linear Tensorface method. This implies that if we apply a more advanced method such as ICA, an even better performance can be achieved. At the end of this paper, we present our experiment result based on the framework, which was performed on the Freiburg face database.
Keywords :
face recognition; independent component analysis; linear algebra; Freiburg face database; appearance-based method; distance function; face recognition; independent component analysis; multilinear algebra; statistical knowledge; tensor projector; Data engineering; Face detection; Face recognition; Image recognition; Lighting; Matrix decomposition; Principal component analysis; Random variables; Tensile stress; Training data;
Conference_Titel :
Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5315-3
DOI :
10.1109/ICBECS.2010.5462474