用VGG-16实现98%准确率的手语识别，助力视障人士交互
VGG Induced Deep Hand Sign Language Detection
- 基于VGG-16与迁移学习，结合图像增强提升识别能力
- 在NUS数据集上达98%准确率，测试集涵盖10类手语动作
- 适合残障辅助技术、实时手语识别系统研发者参考
手语识别是人机交互的重要环节，尤其为视障人群提供沟通基础。本文提出一种新型手语识别系统，采用卷积神经网络VGG-16，在Python与Keras框架下训练模型。使用公开图像数据集进行训练，并以包含10类手势的NUS数据集作为验证集。同时，通过Google开源API构建10类手语的测试数据集。实验结果表明，结合迁移学习与图像数据增强，VGG-16模型达到约98%的识别准确率。
原文摘要 · Abstract (English)
Hand gesture recognition is an important aspect of human-computer interaction. It forms the basis of sign language for the visually impaired people. This work proposes a novel hand gesture recognizing system for the differently-abled persons. The model uses a convolutional neural network, known as VGG-16 net, for building a trained model on a widely used image dataset by employing Python and Keras libraries. Furthermore, the result is validated by the NUS dataset, consisting of 10 classes of hand gestures, fed to the model as the validation set. Afterwards, a testing dataset of 10 classes is built by employing Google's open source Application Programming Interface (API) that captures different gestures of human hand and the efficacy is then measured by carrying out experiments. The experimental results show that by combining a transfer learning mechanism together with the image data augmentation, the VGG-16 net produced around 98% accuracy.
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