Observatorio de I+D+i UPM

Descripción
Nowadays, Convolutional Neural Nets (CNNs) have become the reference technology for many computer vision problems, including facial landmarks detection. Although CNNs are very robust, they still lack accuracy because they cannot enforce the estimated landmarks to represent a valid face shape. In this paper we investigate the use of a cascade of CNN regressors to make the set of estimated landmarks lie closer to a valid face shape. To this end, we introduce CRN, a facial landmarks detection algorithm based on a Cascade of Recombinator Networks. The proposed approach not only improves the baseline model, but also achieves state-of-the-art results in 300W, COFW and AFLW that are widely considered the most challenging public data sets.
Internacional	Si
Nombre congreso	Proceedings Iberoamerican Congress on Pattern Recognition, CIARP 2018
Tipo de participación	960
Lugar del congreso
Revisores	Si
ISBN o ISSN	978-3-030-13468-6
DOI
Fecha inicio congreso	19/11/2018
Fecha fin congreso	22/11/2018
Desde la página	575
Hasta la página	583
Título de las actas	A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment

Esta actividad pertenece a memorias de investigación

• Autor: Pedro Diego Lopez Maroto (UPM)
• Autor: Roberto Valle Fernandez (UPM)
• Autor: Luis Baumela Molina (UPM)

• Creador: Grupo de Investigación: Visión por Computador y Robótica Aérea
• Departamento: Inteligencia Artificial