Advances in Face Detection and Facial Image Analysis by Michal Kawulok, Emre Celebi, Bogdan Smolka

By Michal Kawulok, Emre Celebi, Bogdan Smolka

This ebook provides the state of the art in face detection and research. It outlines new study instructions, together with specifically psychology-based facial dynamics reputation, aimed toward a number of purposes resembling habit research, deception detection, and prognosis of assorted mental problems. subject matters of curiosity contain face and facial landmark detection, face popularity, facial features and emotion research, facial dynamics research, face category, identity, and clustering, and gaze path and head pose estimation, in addition to purposes of face analysis.

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72 lighting conditions. Also, the reported results of ‘cone-cast’, ‘harmonic images-cast’ and ‘9PL-real’ showed that better results can be obtained when cast shadows were considered. Although paper [28, 29] also use one image to adjust lighting conditions, they need to recover the 3D model of the face first. Our algorithm is a completely 2D-based approach. Computationally, it is much less expensive compared with those 3D based methods. The basis images of a subject can be directly computed with Eq.

Lighting variations will also bring challenges for the processing of image sequences. Even though there are a lot of algorithms to deal with lighting problem for images, there are a few investigations for image sequences particularly. In fact most lighting algorithms are too complicated for image sequences, and do not consider the special characteristic of sequences. Also, the scene can be more complex in real applications. Thus it is impossible to build lighting models for different kind of subjects and adjust the lighting condition for each of them independently.

Inform. 10(1), 1–13 (2014) 2. C. Zhang, Z. Zhang, A survey of recent advances in face detection. Microsoft Research Technical Report, MSR-TR-2010-66, June 2010 3. Z. Zeng, M. I. S. Huang, A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009) 32 L. Nanni et al. 4. L. S. Q. Lu, Face detection using one-class based support vectors, in Proceedings 6th IEEE International Conference Automatic Face Gesture Recognition, Hoboken, NJ 07030 USA, (2004), pp.

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