Abstract
In this work we present a new 3D face recognition method based on orientation maps. The proposed model consists of a method for extracting distinctive features from range images of face that can be used to perform reliable matching between different poses of a face. For a 3D face scan, range image is computed and the potential interest points are identified by searching at all scales. Based on the stability of the interest point, significant points are extracted. For each significant point, we compute the significant point descriptor which consists of vector made of values from the convolved orientation maps located on concentric circles centred on the significant point, and where the amount of Gaussian smoothing is proportional to the radii of the circles. Experiments have been conducted on the standard 3D face image database. Experiments show that the newly proposed method provides higher recognition rate compared to other existing contemporary models developed for 3D face recognition.
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Shekar, B.H., Harivinod, N., Kumari, M.S. (2011). 3D Face Recognition Using Orientation Maps. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_41
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DOI: https://doi.org/10.1007/978-3-642-22720-2_41
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