Pose-Invariant Hand Shape Recognition Based on Finger Geometry
Pose-Invariant Hand Shape Recognition Based on Finger Geometry
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DOI：
10.1109/tsmc.2014.2330551
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Wenxiong Kang;Qiuxia Wu
中科院分区：
文献类型：
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作者：
Wenxiong Kang;Qiuxia Wu
提出了一种基于手指几何形状的姿态不变手形识别方法。首先，受Yoruk等人提出的分割方法的启发，我们进行了一个新的改进的分割提取手指的区域时，手是在一个自然的姿态。其次，利用傅立叶描述子和手指面积函数分别提取手指边界曲线特征和区域面积。最后，基于加权和的分数级融合得到匹配结果。由于手指分割策略和特征提取方法都具有旋转和平移不变性，因此该方法更适用于自然姿态的手。使用Bogazici University Hand数据库的实验表明，该方法可以实现0.0369的所有数据和0.0273的样本组内角度偏差小于45°的等错误率。因此，所提出的方法是适合于现实世界的应用。
In this paper, a pose-invariant hand shape recognition method based on the geometry of the fingers is proposed. Firstly, inspired by the segmentation method presented by Yoruk et al., we conduct a novel improvement on the segmentation for extracting the region of the fingers when the hand is in a natural pose. Secondly, Fourier descriptors and finger area functions are employed to extract the finger boundary curve features and region areas, respectively. Finally, score-level fusion based on a weighted sum is used to obtain matching results. Because the finger segmentation strategy and the feature extraction method are both rotation and translation invariant, the proposed method is more suitable for a naturally posed hand. Experiments using the Bogazici University Hand database show that the proposed method can achieve an equal error rate of 0.0369 for all data and 0.0273 for samples with an intragroup angle deviation of less than 45°. Thus, the proposed method is suitable for real-world applications.