Title :
Statistical learning and analysis for unconstrained face recognition and analysis
Author_Institution :
Siemens Corp. Res. Inc., Princeton, NJ, USA
Abstract :
Summary form only given. Although face recognition has been actively studied during the nineties, the state-of-the-art recognition systems perform poorly when confronted with unconstrained scenarios such as illumination and pose variations, surveillance video, etc. In this talk, I address these challenges by introducing approaches to recognizing human faces under illumination and pose variations and from video sequences, using statistical learning and analysis techniques. I also talk about how to estimate age from the face image.
Keywords :
face recognition; image sequences; learning (artificial intelligence); statistical analysis; human faces; statistical analysis; statistical learning; unconstrained face recognition; video sequences; Computer vision; Educational institutions; Face recognition; Humans; Image sequence analysis; Lighting; Machine learning; Statistical learning; Surveillance; Video sequences;
Conference_Titel :
Wireless and Optical Communications, 2005. 14th Annual WOCC 2005. International Conference on
Print_ISBN :
0-7803-9000-8
DOI :
10.1109/WOCC.2005.1553782