Sift Research Paper

Sift Research Paper-86
We use cookies to make interactions with our website easy and meaningful, to better understand the use of our services, and to tailor advertising.For further information, including about cookie settings, please read our Cookie Policy .They used Gabor wavelets and multistage model to extract permanent features, and canny edge detection method for transient features. In 2004, Pantic and Rothkrantz [4] proposed a way to recognize facial expressions in front view and profile view using rule based classifier on 25 subjects of face expression with 86% accuracy.

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The expression of human face randomly changes with respect to his mood.

Thus, it becomes more challenging task to compare face under different emotions with only neutral faces which are stored in the database.

The proposed algorithm yields 81.4% accuracy on 164 samples of Cohn-Kanade and only 55% to 68% accuracy for all three methods on 150 samples of JAFFE database. [6] represented micro patterns of face with local binary patterns (LBP).

They performed experiments on the Cohn-Kanade database.

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The experiments are performed on the Japanese Female Facial Expression (JAFFE) database, which indicates that the proposed approach achieves better performance than SIFT based methods.

In addition, it shows robustness against various facial expressions.


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