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 particularly psychology-based facial dynamics reputation, geared toward numerous purposes comparable to habit research, deception detection, and prognosis of varied mental issues. subject matters of curiosity contain face and facial landmark detection, face popularity, facial features and emotion research, facial dynamics research, face category, id, and clustering, and gaze path and head pose estimation, in addition to purposes of face analysis.

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Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society conference on Computer Vision and Pattern Recognition, 1, 511–518 (2001) 6. C. Küblbeck, A. Ernst, Face detection and track in video sequences using the modified census transformation. Image Vis. Comput. 24(6), 564–572 (2006) 7. C. Huang, H. Ai, Y. Li, S. Lao, High-performance rotation invariant multiview face detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 671–686 (2007) 8.

The second best approach is the eye detector EYE. Although it works quite well, it has a high computational time. As a result, it cannot be used in all applications. The other approaches are less useful if taken individually; however, since they do not require a high computational cost, they can be useful in sequence to decrease the number of false positives. g. in face tagging), EYE filtering can be used in the ensemble to further reduce the number of false positives without decreasing the number of true positives.

Then the error term can be estimated according to the lighting coefficients. Finally, the basis images are recovered. In the PIE face database [38], there are 23 images per subject taken under multiple lighting sources, and altogether 69 subjects. We recover 23 sets of the basis images from the 23 images of every subject respectively. With these estimated basis images, we perform recognition on the 1587 images (23 images per person) 23 times. We also estimate basis for images in the AR database [39].

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