True smile recognition using neural networks and simple PCA

Miyoko Nakano, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu, Fumiko Yasukata

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


Recently, an eigenface method by using the principal component analysis (PCA) is popular in a filed of facial expressions recognition. In this study, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. By using Neural Networks (NN), the difference in value of cos θ between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smile, computer simulations are done with real images.

Original languageEnglish
Pages (from-to)631-637
Number of pages7
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2773 PART 1
Publication statusPublished - 2003 Dec 1
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: 2003 Sept 32003 Sept 5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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