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首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Improved differential box counting with multi-scale and multi-direction: A new palmprint recognition method
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Improved differential box counting with multi-scale and multi-direction: A new palmprint recognition method

机译：多尺度，多方向改进的差分盒计数：一种新的掌纹识别方法

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摘要

A novel palmprint recognition called improved differential box counting (IDBC) with multi-scale and multi-directional is proposed in this paper. At present, fractal dimension as feature vectors cannot accurately reflect the characteristics of image information, and the algorithm complexity is high. Firstly we set out to improve the method of differential box counting, putting forward fractal characteristics as eigenvector. Next, for effective description of accurate orientations and scale, we combine multi-scale and multi-direction of Gabor and Curvelet with IDBC (GIDBC and CIDBC), further proving that Curvelet is more effective than Gabor for palmprint recognition. Experimental results on PolyU palmprint experiment show that the proposed method can obtain state-of-the-art recognition accuracy (99.78%), reduce algorithm complexity and meet the real-time requirements that time of feature extraction and matching is less than 300 ms.
机译：提出了一种新颖的掌纹识别方法，称为多尺度多方向改进的差分盒计数（IDBC）。目前，作为特征向量的分形维数不能准确反映图像信息的特征，算法复杂度高。首先，我们着手改进差分盒计数的方法，提出分形特征作为特征向量。接下来，为了有效地描述精确的方向和比例，我们将Gabor和Curvelet的多比例和多方向与IDBC（GIDBC和CIDBC）相结合，进一步证明Curvelet在掌纹识别方面比Gabor更有效。在PolyU掌纹实验上的实验结果表明，该方法可获得最新的识别精度（99.78％），降低了算法复杂度，满足了特征提取和匹配时间小于300 ms的实时性要求。

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