DeSIRe: Deep Signer-Invariant Representations for Sign Language Recognition

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Pedro M. Ferreira (Author) Other; Diogo Pernes (Author) Other; Ana Rebelo (Author) Other; Jaime S. Cardoso (Author) FEUP

Journal

Title: IEEE Transactions on Systems, Man, and Cybernetics: Systems

Pages: 1-16

ISSN: 2168-2216

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00R-HFD

DOI: 10.1109/tsmc.2019.2957347

Abstract (EN): As a key technology to help bridging the gap between deaf and hearing people, sign language recognition (SLR) has become one of the most active research topics in the human–computer interaction field. Although several SLR methodologies have been proposed, the development of a realworld SLR system is still a very challenging task. One of the main challenges is related to the large intersigner variability that exists in the manual signing process of sign languages. To address this problem, we propose a novel end-to-end deep neural network that explicitly models highly discriminative signer-independent latent representations from the input data. The key idea of our model is to learn a distribution over latent representations, conditionally independent of signer identity. Accordingly, the learned latent representations will preserve as much information as possible about the signs, and discard signer-specific traits that are irrelevant for recognition. By imposing such regularization in the representation space, the result is a truly signer-independent model which is robust to different and new test signers. The experimental results demonstrate the effectiveness of the proposed model in several SLR databases.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16