南京大学学报（自然科学版）Issue(1)：174-180,7.DOI:10.13232/j.cnki.jnju.2015.01.024
一种基于数据场和小波包熵的掌纹识别方法
A palmprint recognition method based on data fields and wavelet packet entropy
摘要
Abstract
Palmprint images contain rich unique features for reliable human identification,which makes it a very com-petitive topic in biometric research.From a low resolution palmprint image,the information of principal lines and wrinkles can be obtained to realize palmprint recognition.The direction feature of palmprint lines is an effective feature.But how to effectively fuse the direction feature and other palmprint line features is an open problem in palmprint recognition.In order to solve the problem,a palmprint recognition algorithm based on palmprint field features is proposed in the paper.In the method,data fields and wavelet packed entropy are used to construct palmprint data field and extract a new palmprint feature,the palmprint field feature.The field feature is the combination of the structural feature and direction feature.Firstly,the data field theory is introduced into palmprint recognition field and each point in the palm lines is seen as a data point with unit mass to map an enhanced palmprint image from gray space to the corresponding potential space.In the space,all points in the palm lines will be affected by other points to form a palmprint image data field.Because the distribution of palmprint data field is affected by the thickness,direction and distribution density of palm-lines,a wealth of the structural and direction information of palm-lines are provided by palmprint data field.For the sake of improving the distinguish ability,the palmprint data field data is decomposed into a relative palmprint data field,an absolute palmprint data field and a direction data field.The absolute data field can make a rough distinction between background and targets,the edge information is highlighted in the relative data field and the direction information of points in each palm-line is obtained in the direction data field.Next,the different sub-data fields are decomposed by wavelet packet transform and the entropies for all nodes for wavelet packet are calculated.These wavelet packed entropies can represent the features of energy distribution of different sub-data fields in different nodes.Finally,all of features of each sub-field are joined into one palmprint field feature,which is fed to backpropagation neural networks for classification.The experimental results illustrate the effectiveness of the method.关键词
生物特征识别/掌纹识别/掌纹场特征/数据场/小波包熵Key words
biometrics/palmprint recognition/palmprint field feature/data fields/wavelet packet entropy分类
信息技术与安全科学引用本文复制引用
王艳霞,赵建民,郑忠龙,孙广华..一种基于数据场和小波包熵的掌纹识别方法[J].南京大学学报（自然科学版）,2015,(1):174-180,7.基金项目
国家自然科学基金(61272468,61170109),浙江省自然科学青年基金(Q13F020006),浙江师范大学计算机软件与理论省级重中之重学科开放基金(ZSDZZZZXK27) （61272468,61170109）
