TY - CHAP T1 - 3D Facial Pose Tracking in Uncalibrated Videos T2 - Pattern Recognition and Machine IntelligencePattern Recognition and Machine Intelligence Y1 - 2005 A1 - Aggarwal,Gaurav A1 - Veeraraghavan,Ashok A1 - Chellapa, Rama ED - Pal,Sankar ED - Bandyopadhyay,Sanghamitra ED - Biswas,Sambhunath AB - This paper presents a method to recover the 3D configuration of a face in each frame of a video. The 3D configuration consists of the 3 translational parameters and the 3 orientation parameters which correspond to the yaw, pitch and roll of the face, which is important for applications like face modeling, recognition, expression analysis, etc. The approach combines the structural advantages of geometric modeling with the statistical advantages of a particle-filter based inference. The face is modeled as the curved surface of a cylinder which is free to translate and rotate arbitrarily. The geometric modeling takes care of pose and self-occlusion while the statistical modeling handles moderate occlusion and illumination variations. Experimental results on multiple datasets are provided to show the efficacy of the approach. The insensitivity of our approach to calibration parameters (focal length) is also shown. JA - Pattern Recognition and Machine IntelligencePattern Recognition and Machine Intelligence T3 - Lecture Notes in Computer Science PB - Springer Berlin / Heidelberg VL - 3776 SN - 978-3-540-30506-4 UR - http://dx.doi.org/10.1007/11590316_81 ER -