In this paper we address the problem of automatically locating the facial landmarks of a single person across frames of a video sequence. We propose two methods that utilize Kalman filter based approaches to assist an Active Shape Model (ASM) in achieving this goal. The use of Kalman filtering not only aids in better initialization of the ASM by predicting landmark locations in the next frame but also helps in refining its search results and hence in producing improved fitting accuracy. We evaluate our tracking methods on frames from three video sequences and quantitatively demonstrate their reliability and accuracy. © 2012 Springer-Verlag.
CITATION STYLE
Prabhu, U., Seshadri, K., & Savvides, M. (2012). Automatic facial landmark tracking in video sequences using kalman filter assisted active shape models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6553 LNCS, pp. 86–99). https://doi.org/10.1007/978-3-642-35749-7_7
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