基于嵌套深度学习模型的信息反馈萤火虫算法在视障人士智能手势识别中的设计

IF 1.7 Q2 REHABILITATION
G. Aldehim, Radwa Marzouk, M. Al-Hagery, A. Hilal, Amani A. Alneil
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引用次数: 0

摘要

手势识别是当前技术中一个发展中的课题。重点是利用人机交互的数学方法来检测人类的手势。一些人机交互模式有触摸屏、键盘、鼠标等。在计算机中实现多用途硬件时,所有这些小工具都有其优点和缺点。手势检测是构建用户友好界面的重要方法之一。一般来说,手势是由任何身体状态或动作产生的,但通常来自手或脸。为此,本文设计了一种基于嵌套深度学习的信息反馈萤火虫算法(IFBFFA-NDL)模型,用于视觉障碍者智能手势识别。提出的IFBFFA-NDL技术利用深度学习的概念,在识别过程中采用元启发式超参数调整策略。为了生成特征向量集合,IFBFFA-NDL技术使用NASNet模型。对于NASNet模型的最优超参数选择,采用IFBFFA算法。为了识别不同类型的手势,采用了嵌套的长短期记忆分类模型。为了展示IFBFFA-NDL技术的即兴手势检测效率,我们进行了详细的对比结果分析,结果显示IFBFFA-NDL技术与现有方法相比,识别率提高了99.73%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People
Gesture recognition is a developing topic in current technologies. The focus is to detect human gestures by utilizing mathematical methods for human–computer interaction. Some modes of human–computer interaction are touch screens, keyboard, mouse, etc. All these gadgets have their merits and demerits while implementing versatile hardware in computers. Gesture detection is one of the vital methods to construct user-friendly interfaces. Generally, gestures are created from any bodily state or motion but typically originate from the hand or face. Therefore, this manuscript designs an Information Feedback Firefly Algorithm with Nested Deep Learning (IFBFFA-NDL) model for intelligent gesture recognition of visually disabled people. The presented IFBFFA-NDL technique exploits the concepts of DL with a metaheuristic hyperparameter tuning strategy for the recognition process. To generate a collection of feature vectors, the IFBFFA-NDL technique uses the NASNet model. For optimal hyperparameter selection of the NASNet model, the IFBFFA algorithm is used. To recognize different types of gestures, a nested long short-term memory classification model was used. For exhibiting the improvised gesture detection efficiency of the IFBFFA-NDL technique, a detailed comparative result analysis was conducted and the outcomes highlighted the improved recognition rate of the IFBFFA-NDL technique as 99.73% compared to recent approaches.
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来源期刊
CiteScore
3.20
自引率
0.00%
发文量
13
审稿时长
16 weeks
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