Abstract
Automatic head pose estimation is useful in human computer interaction and biometric recognition. However, it is a very challenging problem. To achieve robust for head pose estimation, a novel method based on depth images is proposed in this paper. The bilateral symmetry of face is utilized to design a discriminative integral slice feature, which is presented as a 3D vector from the geometric center of a slice to nose tip. Random regression forests are employed to map discriminative integral slice features to continuous head poses, given the advantage that they can maintain accuracy when a large proportion of the data is missing. Experimental results on the ETH database demonstrate that the proposed method is more accurate than state-of-the-art methods for head pose estimation.
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Tang, Y., Sun, Z., Tan, T. (2011). Real-Time Head Pose Estimation Using Random Regression Forests. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_9
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DOI: https://doi.org/10.1007/978-3-642-25449-9_9
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