摘要
本文提出了一种采用canny变换来提取虹膜特征,并用小波神经网络来进行分类的虹膜检测识别的新方法。该方法结合了小波神经网络作为一种新的分类方法,它很好地改进了识别精度,同时提高了系统的性能。一个简洁并且能快速训练的算子Adaboost也将在小波神经网络中给出介绍。实验结果表明,应用该算子进行分类识别时,识别率为100%,该方法具有很高效的可行性。
This paper presents an iris detection and recognition method, which adopts Canny transform to extract iris texture feature and wavelet probabilistic neural network as iris biometric classifier. The method combines wavelet neural network and probabilistic neural network for a new classifier model which will be able to improve the biometrics recognition accuracy as well as the global system performance. A simple and fast training algorithm, AdaBoost, is also introduced for training the wavelet probabilistic neural network. In the experimental results show 100% correct classifications when applying the algorithm on an iris images database and the method have an efficiency feasibility and performance.
出处
《生命科学仪器》
2007年第8期37-41,共5页
Life Science Instruments
基金
辽宁省自然科学基金(20062033)
教育部重点实验室基金(PAL200508)
关键词
虹膜识别
特征提取
小波变换
边缘定位
iris recognition, feature extraction, wavelet transform, edge location
