Extreme Learning Machine for Real Time Recognition of Brazilian Sign Language
Fernando M. de Paula Neto, Lucas F. S. Cambuim, Rafael Melo Macieira, Teresa B. Ludermir, Cleber Zanchettin, Edna N. S. Barros · 2015
The quantity of computing application that interacts with users through gesture or body motion has been growing. Among these applications is the sign language recognizer used to help hearing impaired people. This work proposes an architecture able to recognize Brazilian sign language (LIBRAS) in an embedded platform. The system focuses on a simple feature from 'finger spelling expressions' represented by a series of hands gestural images, and uses the Extreme Learning Machine network to classify them. The proposed structure uses camera images only and does not need any gloves or sensors. The obtained results are 5 times faster and 16 times better than classical approaches.