American Sign Language alphabet recognition using Convolutional Neural Networks with multiview augmentation and inference fusion

Abstract

American Sign Language (ASL) alphabet recognition by computer vision is a challenging task due to the complexity in ASL signs, high interclass similarities, large intraclass variations, and constant occlusions. This paper describes a method for ASL alphabet recognition using Convolutional Neural Networks (CNN) with multiview augmentation and inference fusion, from depth images captured by Microsoft Kinect. Our approach augments the original data by generating more perspective views, which makes the training more effective and reduces the potential overfitting. During the inference step, our approach comprehends information from multiple views for the final prediction to address the confusing cases caused by orientational variations and partial occlusions. On two public benchmark datasets, our method outperforms the state-of-the-arts.