Download Advances in Face Detection and Facial Image Analysis by Michal Kawulok, Emre Celebi, Bogdan Smolka PDF
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 acceptance, aimed toward numerous purposes equivalent to habit research, deception detection, and prognosis of varied mental problems. themes of curiosity contain face and facial landmark detection, face acceptance, 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 info for Advances in Face Detection and Facial Image Analysis
Li, S. Lao, High-performance rotation invariant multiview face detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 671–686 (2007) 8. J. Wu, S. D. M. Rehg, Fast asymmetric learning for cascade face detection. IEEE Trans. Pattern Anal. Mach. Intell. 30, 369–382 (2008) 9. M. Anisetti, Fast and robust face detection, in Multimedia Techniques for Device and Ambient Intelligence, Chapter 3 (Springer, US, 2009). ISBN: 978-0-387-88776-0 10. J. Li, Y. Zhang, Learning surf cascade for fast and accurate object detection, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2013) Hoboken, NJ 07030 USA 11.
F. Dominio, M. Donadeo, P. Zanuttigh, Combining multiple depth-based descriptors for hand gesture recognition. Pattern Recogn. Lett. 50, 101–111 (2014) Lighting Estimation and Adjustment for Facial Images Xiaoyue Jiang, Xiaoyi Feng, Jun Wu, and Jinye Peng Abstract For robust face detection and recognition, the problem of lighting variation is considered as one of the greatest challenges. Lighting estimation and adjustment is a useful way to remove the influence of illumination for images. Due to the different prior knowledge provided by a single image and image sequences, algorithms dealing with lighting problems are always different for these two conditions.
Pi / 2 The parameter œ controls the contribution of color and geometry to the final segmentation. High values of œ increase the relevance of geometry, while low values of œ increase the relevance of color information. Notice that at the two extrema the algorithm can be reduced to a color-based segmentation (œ D 0) or to a geometry Á (depth) only segmentation (œ ! 1 . A complete discussion on the effect of this parameter and a method to automatically tune it to the optimal value is presented in .