A practically deployable gesture recognition system is developed using a robust hand detection method implemented using a motion-based image segmentation process and a two-level bare hand classification model, which is integrated with a gesture classification system of 58 gestures using new robust features. Since detection of bare hand is affected by nonideal conditions, multiple color-texture features are analyzed in this study. In the second stage of the system, 18 new ASCII characters are introduced and analyzed along with the existing 40 characters (alphabets, numbers, and arithmetic operators). New 15 dimensional features are introduced along with the existing features to enhance the classification accuracy of the gestures. Significance of features statistically tested using one-way analysis of variance (ANOVA), Kruskal–Wallis and Friedman test, which are sequentially ranked and evaluated using incremental feature selection (IFS) method. Performance of the proposed hand detection system is observed to be 12.5% higher than the existing hand detection system under clean conditions, while 46.4% higher under the nonideal conditions. Performance of 58 gestures classification model has improved by 12.08% (Naïve Bayes), 8.86% (ELM), 10.83% (SVM), 8.02% (kNN), and 6.61% (ANN) after using the new features. Majority voting-based classifier fusion method further improves the performance of the gesture recognition system by 3.88%, which is validated by Turkey’s HSD test.

中文翻译：

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使用强大的 HCI 手部检测方法对虚拟裸手界面系统进行建模

使用基于运动的图像分割过程和两级裸手分类模型实现的稳健手部检测方法开发了一个实际可部署的手势识别系统，该方法与使用新的稳健特征的 58 个手势的手势分类系统集成。由于裸手的检测受非理想条件的影响，本研究分析了多种颜色纹理特征。在系统的第二阶段，引入和分析了 18 个新的 ASCII 字符以及现有的 40 个字符（字母、数字和算术运算符）。与现有特征一起引入了新的 15 维特征，以提高手势的分类准确性。使用单向方差分析 (ANOVA) 统计测试的特征的显着性，Kruskal-Wallis 和 Friedman 检验，使用增量特征选择 (IFS) 方法按顺序排列和评估。在清洁条件下，所提出的手部检测系统的性能比现有的手部检测系统高 12.5%，而在非理想条件下则高 46.4%。58个手势分类模型的性能提升了12.08%（朴素贝叶斯）、8.86%（ELM）、10.83%（SVM）、8.02%（ķNN)，使用新功能后的 6.61% (ANN)。基于多数投票的分类器融合方法进一步将手势识别系统的性能提高了 3.88%，这一点通过土耳其的 HSD 测试得到了验证。