Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible t...Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.展开更多
基金Project(No.R112002105070020(2010))supported by the National Research Foundation of Korea(NRF) through the Biometrics Engi-neering Research Center(BERC)at Yonsei University
文摘Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.
文摘在指静脉识别中,如何利用卷积神经网络提取具有类间分离和类内聚合的静脉特征是当前的研究热点,为此提出了在卷积神经网络中采用中心损失(center loss)用于减小指静脉的类内距离,同时采用softmax loss用于约束类间距离,以此作为网络的损失函数。为了进一步提高模型的表达能力,采用swish激活函数代替线性整流(rectified linear unit,ReLU)激活函数,在此基础上,将全局平均池化层的输出向量进行归一化操作后作为指静脉的特征向量,减少网络参数的同时保留了指静脉的高级语义信息。结果表明:改进后的指静脉识别算法在FV-USM和MMCBNU-6000这2个数据集上识别的准确率分别达到98.23%和98.35%,优于传统的卷积神经网络识别算法。
