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首页> 外文期刊>Journal of visual communication & image representation >Signer-independence finger alphabet recognition using discrete wavelet transform and area level run lengths
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Signer-independence finger alphabet recognition using discrete wavelet transform and area level run lengths

机译：使用离散小波变换和区域级游程长度的独立于签署人的手指字母识别

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This paper proposes a method for finger alphabet recognition from backhand images with signer independence. Input images that are divided into fist sign and non-fist sign groups should be analyzed and processed in different ways. Finger alphabets in the fist group are represented by a one-dimensional signal that represents the external hand boundaries. Its low and high frequency components are then extracted by discrete wavelet transform, which are key features for recognition. The non-fist sign images, which are radically digitized into a 20 x 20 block mask in terms of the hand geometry, due to the hand's physical structure, can be recognized by the patterns of the occupied blocks. The experimental results show that the proposed method has a high likelihood of differentiating twenty-three static finger alphabets of backhand images. The proposed method reaches an improvement of 27.86% in recognition accuracy on a significant dataset of fist signs that includes multiple users, while the statistical distribution of the area level run length algorithm outperforms previous forehand approaches by 89.38% in recognition accuracy. (C) 2016 Elsevier Inc. All rights reserved.
机译：提出了一种具有签名者独立性的反手图像手指字母识别方法。分为拳头符号组和非拳头符号组的输入图像应以不同方式进行分析和处理。拳头组中的手指字母由代表外部手部边界的一维信号表示。然后通过离散小波变换提取其低频和高频分量，这是识别的关键特征。由于手的物理结构，根据手的几何形状将其基本数字化为20 x 20块遮罩的非拳头符号图像可以通过所占据的块的图案来识别。实验结果表明，该方法具有较高的区分反手图像的二十三个静态手指字母的可能性。在包含多个用户的拳头信号的重要数据集上，该方法的识别准确率提高了27.86％，而区域级别游程算法的统计分布在识别准确度方面比以前的正手方法高出89.38％。 （C）2016 Elsevier Inc.保留所有权利。

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