Advances in Pattern Recognition: Second Mexican Conference by Pavel Herrera-Domínguez, Leopoldo Altamirano-Robles (auth.), PDF

By Pavel Herrera-Domínguez, Leopoldo Altamirano-Robles (auth.), José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, Josef Kittler (eds.)

ISBN-10: 3642159915

ISBN-13: 9783642159916

ISBN-10: 3642159923

ISBN-13: 9783642159923

This booklet constitutes the completely refereed court cases of the second one Mexican convention on trend acceptance, MCPR 2010, held in Puebly, Mexico, in September 2010. The 39 revised papers have been rigorously reviewed and chosen from 89 submissions and are geared up in topical sections on machine imaginative and prescient and robotics, picture processing, neural networks and sign processing, trend attractiveness, info mining, usual language and rfile processing.

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Extra info for Advances in Pattern Recognition: Second Mexican Conference on Pattern Recognition, MCPR 2010, Puebla, Mexico, September 27-29, 2010. Proceedings

Example text

As a result there have been attempts to compute facial shape invariants that can be used to recognise a face. It has been shown that statistical models based on the distribution of surface normals can offer a powerful means of representing and recognising facial shape. For instance, Smith and Hancock [1] project the surface normals into a tangent space to construct a statistical model using principal geodesic analysis. This work has recently been extended to gender recognition [2], but has proved too cumbersome for expression recognition.

The obtained results indicate a correct assessment of the intensity about 90% for Surprise emotion. For other expressions such as joy, sadness, anger, and fear the percentage of corresponding correct recognition is about 80, 85, 77, and 75% respectively. Comparing the results of facial expression recognition with other well-known systems the proposed approach gives average value about 79% against 72% of Esau [4]. The high degree of recognition mainly depends on the number of AUs or FAPs used for description of emotion.

In: 11th IEEE Int. Conf. on Tools with Artif. , p. 113 (1999) 6. : Coding facial expressions with Gabor wavelets. Y. (1998) 7. : Real-Time Expression Recog. Using Active Appearance Model. In: Int. Conf. Comp. Intelligence and Security, China, pp. 1–8 (2006) 8. : Facial Expression Classification Using PCA and Hierarchical Radial Basis Function Network. J. Inf. Science and Eng. 22, 1033–1046 (2006) 9. : Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion.

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Advances in Pattern Recognition: Second Mexican Conference on Pattern Recognition, MCPR 2010, Puebla, Mexico, September 27-29, 2010. Proceedings by Pavel Herrera-Domínguez, Leopoldo Altamirano-Robles (auth.), José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, Josef Kittler (eds.)


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