Title :
Frontal face classifier using AdaBoost with MCT features
Author :
Yoon, Jongmin ; Kim, Daijin
Author_Institution :
Dept. of Comput. Sci. & Eng., POSTECH, Pohang, South Korea
Abstract :
In this paper, we describe how to classify frontal face from the results of face detection which include non-frontal faces. To do this, we use AdaBoost learning method with Modified Census Transform (MCT) to construct a two-class classifier. As a result of that, our frontal face classifier achieves high classification rate above 96% and fast performance about 10 frames/sec in mobile device.
Keywords :
face recognition; image classification; learning (artificial intelligence); transforms; AdaBoost; frontal face classifier; modified census transform; Classification algorithms; Detectors; Face; Feature extraction; Lighting; Training; Transforms; MCT; adaboost; frontal face classification;
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-7814-9
DOI :
10.1109/ICARCV.2010.5707954
