Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision in Next Generation Image and Video Coding
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Multi-Modal Tracking of Faces for Video Communications
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multi-cue-based CamShift guided particle filter tracking
Expert Systems with Applications: An International Journal
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While computing power and transmission bandwidth have both been steadily increasing over the last few years, bandwidth rather than processing power remains the primary bottleneck for many complex multimedia applications involving communication. Current video coding algorithms use intelligent encoding to yield higher compression ratios at the cost of additional computing requirements for encoding and decoding. The use of techniques from the fields of computer vision and robotics such as object recognition, scene interpretation, and tracking can further improve compression ratios as well as provide additional information about the video sequence being transmitted. We used a new face tracking system developed in the robotics area to normalize a video sequence to centered images of the face. The face-tracking allowed us to implement a compression scheme based on Principal Component Analysis (PCA), which we call Orthonormal Basis Coding (OBC). We designed and implemented the face tracker and video codecs entirely in software. Our current implementation of OBC operates on recorded video sequences, making it appropriate for applications such as video email.