基于指节纹的个人身份自动识别

人体的指节纹具有稳定性且对于不同的人具有不同的位置和结构特征, 可作为身份识别的依据. 本文提出了一种基于指节纹的身份识别新方法: 对采集的手掌图像首先通过预处理分割出各手指并旋转至水平位置; 然后用Sobel算子求其水平梯度, 对梯度图二值化后经垂直投影得到一维向量; 对此向量应用小波去噪, 生成手指指节纹特征向量; 通过用余弦函数计算指节纹特征向量之间相似度实现最后的匹配. 本文用该方法对来自190个手掌的1900个样本进行了测试, 取得了0.67%的等误率, 单次匹配时间低于2ms. 实验结果表明该方法具有较高的识别精度, 而且识别速度快, 适合在大规模手掌库中实现手掌筛选.

Automatic Personal Authentication Based on Finger Phalangeal Prints

Finger phalangeal prints are stable and unique for each individual, thus can be used in personal authentication. A novel personal recognition method is proposed based on the finger phalangeal print in this paper. The captured image is first preprocessed to segment out fingers and rotate them to the horizontal direction; Sobel operator is used to get the horizontal gradient which is binarized and projected to the horizontal axis. The 1-D projection is then denoised with wavelet and downsampled to get the feature vector. Finally, the similarity of the two matching vectors is measured by using cosine function. The proposed method was tested on the database which contains 1900 samples from 190 different palms. The equal error rate (EER) was no more than 0.67% and one match time consumption was less than 2ms. Experiments suggested that a high recognition accuracy and efficient matching performance can be achieved with the proposed algorithm, which can meet the requirement of real time searching in large palm database.