By Michal Kawulok, Emre Celebi, Bogdan Smolka
This e-book provides the state of the art in face detection and research. It outlines new learn instructions, together with specifically psychology-based facial dynamics popularity, aimed toward a number of purposes corresponding to habit research, deception detection, and prognosis of varied mental issues. issues of curiosity contain face and facial landmark detection, face popularity, facial features and emotion research, facial dynamics research, face class, identity, and clustering, and gaze path and head pose estimation, in addition to functions of face analysis.
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Extra resources for Advances in Face Detection and Facial Image Analysis
1–6 20. Y. Taigman, M. , Ranzato, L. Wolf, Deepface: Closing the gap to human-level performance in face verification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2014), Hoboken, NJ 07030 USA, pp. 1701–1708 21. O. Jesorsky, K. Kirchberg, R. Frischholz, in Face Detection Using the Hausdorff Distance, eds. by J. Bigun, F. Smeraldi. Audio and Video based Person Authentication—AVBPA, (Springer, 2001), Berlin, Germany, pp. 90–95 22. N. Markuš, M. S. Pandži´c, J. Ahlberg, R.
Under similar illumination, images of different subjects will appear almost the same. The difference between the images of the same subject under different illuminations is always larger than that between the images of different subjects under the same illumination . Therefore we can estimate the lighting coefficients of a novel image with an interpolation method. The kernel regression is a smooth interpolation method . It is applied to estimating the lighting coefficients. For every training image, we have their corresponding lighting coefficients.
The matrix Q. Then the projection of probe image I to the subspace spanned by B is QQT I, and the distance between the probe image I and the subspace spanned by B can be computed as kQQT I Ik2 . In the recognition procedure, the probe image is identified as the subspace with minimum distance from it. 5 Experiments on Lighting Estimation The statistical model is trained by images from the extended Yale face database B . With the trained statistical model, we can reconstruct the lighting subspace from only one gallery image, which should be insensitive to lighting variation.
Advances in Face Detection and Facial Image Analysis by Michal Kawulok, Emre Celebi, Bogdan Smolka