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A training-free nose tip detection method from face range images

Xiaoming Peng, Mohammed Bennamoun, Ajmal S. Mian
School of Automation Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, Sichuan 610054, China
Pattern Recognition, Volume 44, Issue 3, March 2011, Pages 544-558 (24 September 2010)

@article{peng2010training,

   title={A training-free nose tip detection method from face range images},

   author={Peng, X. and Bennamoun, M. and Mian, A.S.},

   journal={Pattern Recognition},

   issn={0031-3203},

   year={2010},

   publisher={Elsevier}

}

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Nose tip detection in range images is a specific facial feature detection problem that is highly important for 3D face recognition. In this paper we propose a nose tip detection method that has the following three characteristics. First, it does not require training and does not rely on any particular model. Second, it can deal with both frontal and non-frontal poses. Finally, it is quite fast, requiring only seconds to process an image of 100-200 pixels (in both x and y dimensions) with a MATLAB implementation. A complexity analysis shows that most of the computations involved in the proposed algorithm are simple. Thus, if implemented in hardware (such as a GPU implementation), the proposed method should be able to work in real time. We tested the proposed method extensively on synthetic image data rendered by a 3D head model and real data using FRGC v2.0 data set. Experimental results show that the proposed method is robust to many scenarios that are encountered in common face recognition applications (e.g. surveillance). A high detection rate of 99.43% was obtained on FRGC v2.0 data set. Furthermore, the proposed method can be used to coarsely estimate the roll, yaw and pitch angles of the face pose.
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