A Data Augmentation and Pre-processing Technique for Sign Language Fingerspelling Recognition
| Autor: | Frank Fowley, Ellen Rushe, Anthony Ventresque |
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| Rok vydání: | 2022 |
| Zdroj: | 24th Irish Machine Vision and Image Processing Conference. |
| DOI: | 10.56541/xbav3102 |
| Popis: | The reliance of deep learning algorithms on large scale datasets is a significant challenge for sign language recognition (SLR). The shortage of data resources for training SLR models inevitably leads to poor generalisation, especially for low-resource languages. We propose novel data augmentation and preprocessing techniques based on synthetic data generation to overcome these generalisation difficulties. Using these methods, our models achieved a top-1 accuracy of 86.7% and a top-2 accuracy of 95.5% when evaluated against an unseen corpus of Irish Sign Language (ISL) fingerspelling video recordings. We believe that this constitutes a state-of-the-art performance baseline for an Irish Sign Language recognition model when tested on an unseen dataset. |
| Databáze: | OpenAIRE |
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