| Autor: |
Aguileta AA; Facultad de Matemáticas, Universidad Autónoma de Yucatán, Mérida 97110, Mexico., Brena RF; School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico.; Departamento de Computación y Diseño, Instituto Tecnológico de Sonora, Ciudad Obregón 85000, Mexico., Molino-Minero-Re E; Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas-Unidad Yucatán, Universidad Nacional Autónoma de México, Sierra Papacal, Yucatán 97302, Mexico., Galván-Tejada CE; Unidad Académica de Ingeniería Eléctrica y Comunicaciones, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico. |
| Abstrakt: |
Automatic identification of human facial expressions has many potential applications in today's connected world, from mental health monitoring to feedback for onscreen content or shop windows and sign-language prosodic identification. In this work we use visual information as input, namely, a dataset of face points delivered by a Kinect device. The most recent work on facial expression recognition uses Machine Learning techniques, to use a modular data-driven path of development instead of using human-invented ad hoc rules. In this paper, we present a Machine-Learning based method for automatic facial expression recognition that leverages information fusion architecture techniques from our previous work and soft voting. Our approach shows an average prediction performance clearly above the best state-of-the-art results for the dataset considered. These results provide further evidence of the usefulness of information fusion architectures rather than adopting the default ML approach of features aggregation. |
