基于类卷积神经网络的可见光虹膜识别方法

Iris recognition of visible light based on analogous convolutional neural network

针对应用于智能移动设备的虹膜识别系统在可见光采集条件下虹膜图像受干扰严重使得识别率正确率降低和算法鲁棒性变差的问题,提出一种类卷积神经网络结合局部特征提取的虹膜识别方法。首先,采用暗通道图像去雾算法对归一化虹膜图像进行增强处理以减弱光干扰;然后,利用类卷积神经网络对图像进行降维,获得虹膜的二值化纹理信息;再经分块处理方法提取降维图像各区域局部虹膜纹理信息以构建特征向量;最后用欧氏距离分类器进行匹配识别。为验证算法性能,采用MICHE-I虹膜图库中由iPhone5拍摄所得的30人240张(每人4张室内和4张室外)虹膜图像进行测试,并与Gabor变换和主成分分析虹膜识别方法进行对比。结果表明,该方法在室内外图像均进行训练的条件下正确率能够达到98.33%,且对室内外不同光照变化干扰有较好的鲁棒性,上述性能皆优于Gabor变换和PCA算法。说明本文算法能够满足移动设备虹膜识别使用要求。

The iris images collected by iris recognition system used for smart mobile devices are seriously interfered under visible light. The interference reduces recognition accuracy and decreases the robustness. An iris recognition method based on analogous convolutional neural network and local feature extraction is proposed. Firstly, a haze removal algorithm using dark channel is utilized to enhance the iris texture and reduce the light interference. Then, the analogous convolutional neural network is used to reduce the image dimension, and the texture information is obtained with a binary image. The feature vector of the iris image is built by local feature extraction of the regions of the lower dimension image. Finally, Euclidean distance is utilized in matching process. To validate the performance of the proposed method, the 30 people＇ s 240 iris images （four indoor images for each person and four outdoor images for each person） in MICHE-I iris gallery are tested, and a comparison is conducted with iris recognition methods of Gabor transform and Principal Component Analysis （PCA）. The results show that the recognition accuracy under the condition of both indoor and outdoor images can reach 98.33% and the approach has better robustness under the indoor and outdoor light interference. The performance is superior to the Gabor transform and PCA methods. These demonstrate that the proposed method can satisfy the requirements of iris recognition on mobile device.