In this paper, a lightweighted Intensive Feature Extrication Deep Network (ExtriDeNet) is proposed for precise hand gesture recognition (HGR). ExtriDeNet primarily consists of two blocks: Intensive Feature Fusion Block (IFFB) and Intensive Feature Assimilation Block (IFAB). IFFB incorporates two different scaled filters \(3\times 3\) and \(5 \times 5\) to capture contextual features of hands, while IFAB is designed by embedding influential features of IFFB with two extreme minute and high-level feature responses from two receptive fields generated by employing \(1\times 1\) and \(7\times 7\) sized filters, respectively. The combination of multiscaled filters enriches the network with the most significant features and enhances the learnability of the network. Thus, the proposed ExtriDeNet efficiently defines the distinctive features of different hand gesture classes and achieves high performance as compared to state-of-the-art HGR approaches. The performance of the proposed network is evaluated on the standard datasets: MUGD, Finger Spelling, OUHands, NUS-I, NUS-II and HGR1 for both subject-dependent and subject-independent scheme.

在本文中，提出了一种用于精确手势识别（HGR）的轻量级密集特征提取深度网络（ExtriDeNet）。ExtriDeNet 主要由两个块组成：密集特征融合块 (IFFB) 和密集特征同化块 (IFAB)。IFFB 结合了两个不同的缩放过滤器\(3\times 3\)和\(5 \times 5\)来捕捉手的上下文特征，而 IFAB 的设计是通过嵌入 IFFB 的有影响力的特征与两个极端的分钟和高级特征响应从使用\(1\times 1\)和\(7\times 7\)生成的两个感受野大小的过滤器，分别。多尺度滤波器的组合使网络具有最显着的特征，并增强了网络的可学习性。因此，与最先进的 HGR 方法相比，所提出的 ExtriDeNet 有效地定义了不同手势类别的独特特征并实现了高性能。所提出的网络的性能在标准数据集上进行评估：MUGD、Finger Spelling、OUHands、NUS-I、NUS-II 和 HGR1，适用于主题相关和主题独立方案。