Multiscaled Multi-Head Attention-based Video Transformer Network for Hand Gesture Recognition

Abstract

Dynamic gesture recognition is one of the challenging research areas due to variations in pose, size, and shape of the signer's hand. In this letter, Multiscaled Multi-Head Attention Video Transformer Network (MsMHA-VTN) for dynamic hand gesture recognition is proposed. A pyramidal hierarchy of multiscale features is extracted using the transformer multiscaled head attention model. The proposed model employs different attention dimensions for each head of the transformer which enables it to provide attention at the multiscale level. Further, in addition to single modality, recognition performance using multiple modalities is examined. Extensive experiments demonstrate the superior performance of the proposed MsMHA-VTN with an overall accuracy of 88.22\% and 99.10\% on NVGesture and Briareo datasets, respectively.

References in corpus (6)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition
• Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
• Multi-modal Fusion for Single-Stage Continuous Gesture Recognition
• Res3ATN -- Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos
• Multi-Task and Multi-Modal Learning for RGB Dynamic Gesture Recognition

Cited by in corpus (1)

• A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities