CAJ | 학술논문

为了提高掌纹识别的速度和准确率,克服Contourlet变换在处理高维信号时的不足,提出了一种新的掌纹识别算法.该算法首先对掌纹图像进行àtrous-Contourlet变换,得到高频分量和不同方向不同子带上的低频分量,再根据不同子带的能量分布所提取出的统计特征选择不同的特征加权系数,对图像所得到的不变矩向量进行加权计算,得到新的特征向量,完成掌纹图像的识别.实验结果表明,该算法与小波矩算法、Hu不变矩算法和Contourlet算法相比有较高的效率和匹配精度.
위료제고장문식별적속도화준학솔,극복Contourlet변환재처리고유신호시적불족,제출료일충신적장문식별산법.해산법수선대장문도상진행àtrous-Contourlet변환,득도고빈분량화불동방향불동자대상적저빈분량,재근거불동자대적능량분포소제취출적통계특정선택불동적특정가권계수,대도상소득도적불변구향량진행가권계산,득도신적특정향량,완성장문도상적식별.실험결과표명,해산법여소파구산법、Hu불변구산법화Contourlet산법상비유교고적효솔화필배정도.
In order to improve the speed and accuracy of palmprint identification system and overcome the weakness of Contourlet transform in dealing with high-dimensional signals, a novel identification algorithm of palmprint feature was proposed. Firstly, the àtrous-Contourlet transformation for palmprint image was decomposed to obtain high frequency coefficients and low frequency coefficients in the different sub-band of different direction. Then, different feature weighting coefficients according to the statistical characteristics extracted from different frequency domains were chosen, which were calculated to get some new invariant moments vectors. Finally, the palmprint identification was achieved. The experimental results show that the method here is more effective in matching than wavelet moment algorithm, Hu's invariant moment algorithm and Contourlet algorithm.