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Face Recognition Technology Presentation outlines
- Face Segmentation/Detection
- Facial Feature extraction
- Face Recognition
- Video-based Face Recognition
- and more
Face Recognition Technology
Before the middle 90’s, the research attention was only focused on single-face segmentation. The approaches included:
Deformable feature-based template
Using skin color
During the past ten years, considerable progress has been made in multi-face recognition area, includes:
Example-based learning approach by Sung and Poggio (1994).
The neural network approach by Rowley et al. (1998).
Support vector machine (SVM) by Osuna et al. (1997).
Neural network (NN)
Kanade et al. first proposed an NN-based approach in 1996.
Although NN have received significant attention in many research areas, few applications were successful.
It’s easy to train a neural network with samples which contain faces, but it is much harder to train a neural network with samples which do not.
The number of “non-face” simples are just too large.
Neural network-based filter. A small filter window is used to scan through all portions of the image, and to detect whether a face exists in each window.
Merging overlapping detections and arbitration. By setting a small threshold, many false detections can be eliminated.
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|Title||: Face Recognition Technology | PPT Slides Download|
|Category||: Information Technology|
|Date||: Sunday, December 30th 2012.|
|Download||: Download Face Recognition Technology | PPT Slides Download PPT #1814|