Automated face analysis : emerging technologies and research by Daijin Kim, Jaewon Sung

By Daijin Kim, Jaewon Sung

Considering learn on face acceptance begun within the 1960's, the sector has speedily widened to automatic face research together with face detection, facial gesture attractiveness, and facial features attractiveness.

Automated Face research: rising applied sciences and Research offers theoretical historical past to appreciate the general configuration and difficult challenge of computerized face research structures, that includes a accomplished evaluate of contemporary learn for the sensible implementation of the research method. A must-read for practitioners and scholars within the box, this publication offers knowing through systematically dividing the topic into numerous subproblems corresponding to detection, modeling, and monitoring of the face.

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By Daijin Kim, Jaewon Sung

Considering learn on face acceptance begun within the 1960's, the sector has speedily widened to automatic face research together with face detection, facial gesture attractiveness, and facial features attractiveness.

Automated Face research: rising applied sciences and Research offers theoretical historical past to appreciate the general configuration and difficult challenge of computerized face research structures, that includes a accomplished evaluate of contemporary learn for the sensible implementation of the research method. A must-read for practitioners and scholars within the box, this publication offers knowing through systematically dividing the topic into numerous subproblems corresponding to detection, modeling, and monitoring of the face.

Show description

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Sung, K. (1996). Learning and example selection for object and pattern recognition. PhD thesis, MIT, AI Lab, Cambridge. , & Poggio, T. (1998). Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 39–51. , & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, (pp. 511-518). , & Jones, M. (2002). Fast and robust classification using asymmetric adaboost and a detector cascade.

9. 10. Correlation maps corresponding to (a) eye and (b) non-eye that the distance between the iris center point and the inner point along the horizontal line is equal to the predetermined size of the eye model. Third, we re-scale the vertical size of the eye image in the same scale. The predetermined size of the eye model is given in Fig. 11. In the test databases, the accurate of the our face detection algorithm is 100%. So, we analyze only the accurate of the novel eye detection method in the experiments.

2 (a)). Therefore, The MCT values can be very different even though the only one pixel location is different each other as shown in Fig. 2, where the MCT values of (b) and (c) are 146 and 402, respectively. Also, the MCT is not an entity of magnitude but a pattern of local intensity variation between a pixel and its neighboring pixels. So, the decoded value of the MCT is not appropriate for measuring the difference between two MCT patterns. To solve this problem, we propose the idea of the MCT-based pattern and the MCT-based pattern correlation based on the Hamming distance that measures the difference between two MCT-based patterns.

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