Next Generation Face Tracking Technology Using AAM Techniques

Peter Corcoran, Mircea C. Jonita, Joana Bacivarov
2007 2007 International Symposium on Signals, Circuits and Systems  
This paper realizes an investigation of the Active The third tracking category, statistical inference approaches, Appearance Model (AAM) techniques, with particular emphasis includes Kalman filtering techniques for uni-modal Gaussian on problems related to the implementation of AAM in realrepresentations [18] [19] Monte Carlo approaches for nontime face tracking applications for embedded systems. The paper reprsentaonsi[18] [19], Mont Carl approacesi forn includes (a) new training and model
more » ... truction approaches for Gaussin noar get t nd yw AAMs with increased robustness to head pose variations and inference approaches [21] . changes in the illumination conditions, and (b) a corresponding As technology progresses, the embedded imaging research AAM-based face tracking scheme. starts to focus more and more on extending existing camera functionality to include embedded computer vision. It is already anticipated that machine vision will migrate from in-Arrived already at a stage of implementation and comdustrial applications to consumer and commercial domain. An mercialization in digital cameras, face-tracking still remains embedded computer vision application should meet constraints an important and challenging task in Computer Vision. A on issues like real-time performance, power consumption, or major difficulty in face-tracking is the potential variability of memory requirements. human faces over time due to factors like: variation in pose or In this paper we propose an AAM-based face tracking
doi:10.1109/isscs.2007.4292639 fatcat:j3a2c36e55evto5nqiqy6iitbu