The current popular technique in palmprint recognition - - - print feature based palmprint recognition technique is introduced via a review of palmprint recognition research history 通过对掌纹识别技术研究史的回顾,本文介绍了掌纹识别方面的主流技术- - -基于纹线特征的掌纹识别技术。
After the discussion of the line based and texture energy based palmprint recognition in detail , we analyze the advantages and disadvantages of these two algorithms and suggest an efficient combination of these two ways - - - two - level palmprint recognition 在详细阐述了基于线特征和基于纹理能量的掌纹识别方法之后,我们分析了这两种算法的优缺点,并给出了两种方法的有效结合- - -两层的掌纹识别方法。
In this paper , two kinds of feature extraction methods are proposed based on former work of palmprint recognition and the characteristics of inner palm ; the design and implementation of these two algorithms as well as the test of performance are given 本文在分析现有掌纹识别方法的基础上,结合手掌内表面的特点提出了两种新的掌纹特征提取方法,并分别给出了两种识别算法的设计与实现过程,以及性能测试。
Independent component analysis has particular advantages in image processing . used for palmprint feature extraction , it can ensure the components to be irrelevant and statistical independent among each other , and can more roundly describe the essent . . 独立分量分析方法在图像处理中具有独特的优势，用于掌纹特征提取，使得变换后的各分量之间不仅互不相关，而且还尽可能的统计独立，能更全面的揭示掌纹特征间的本质结构。
According to the speciality of palmprint , this paper proposes a recognition method based on algebraic feature . in order to extract the global feature of palmprint by pca ( principal component analysis ) , k - l transform and nn ( linear neural network ) method is presented 本文根据掌纹本身的特点，结合所处理的掌纹图像的特点，提出有针对性的pca ( principalcomponentanalysis )特征提取方法，分别应用了k - l变换方法，线性神经网络方法对掌纹的全局特征进行提取。
As palmprint is typical of ease acquisition , and its major feature is apparent , stability and divisibility , palmprint automatic identification is a very potential identification technology . however , the research and application of biological feature identification based on palmprint are very few due to the complexity , diversity and variableness of palmprint 掌纹由于易于获取，主特征明显、稳定、具有可分性等特点，以及掌纹自动识别系统具有直接、友好、方便、良好的唯一性和应用范围广的优点，因此是一种很有发展潜力的身份识别方法。
In order to deal with the unknown transformations of samples as a result of preprocessing and improve the system recognition performance further , a palmprint feature extraction method based on template learning in wavelet domain is designed and proposed . this algorithm learns the ideal templates of different classes from palmprint samples , whose parameters are regarded as 该算法从掌纹样本中学习不同类别的理想模板,并将理想模板的参数作为特征用于模式分类,由于考虑了子图样本内在存在的平移和旋转变换,不同类型小波系数对特征提取的贡献,因此达到非常好的识别效果。
Many common biometric features , such as fingerprint , face , iris , palmprint , voice , signature etc . , have the properties that they are possessed by everyone , are different with different person and keep stable in a long period . they cannot be stolen nor lost , fake and are difficult to be forged or imitated . therefore , they are very suitable for the personal authentication 常见的人体生物特征包括:指纹、人脸、虹膜、掌纹、语音、签名等,这些人体生物特征通常具有“人人拥有、人人不同、长期不变”的特点,并且不易遗忘和丢失,也难以伪造和模仿,所以很适合用来进行身份识别或鉴别。
Up to now , many traits in human body have been applied , such as fingerprint , palmprint , iris , face , hand shape and signature , etc . in the thesis , we propose a novel personal identification technology base on hand back and develop a prototype of hand back based identification in this paper 截至目前,已经用于身份识别的人体生物特征包括指纹、掌纹、虹膜、人脸、手形、签名等。在本论文中,研究了一种新的生物特征识别技术? ?手背识别技术,并实现了手背识别系统原型。
The paper abstracts out the palmprint mainline characteristic owing to palmprint image gradation by the gray value characteristic property method , defining the online palmprint characteristic space with the form of the polar coordinate and compressing the dimension , at last we use the neural network to accomplish the design of identification authentication system with the on - line palmprint as the mating algorithm . based on this , a fast and reliable system of identification authentication can be built 采用基于掌纹图像灰度特性的方法提取出掌纹的主线特征,以极坐标的形式定义在线掌纹的特征空间并降维处理,最后用神经网络作为匹配算法完成在线掌纹身份验证系统的设计,以此为基础建立了快速、可靠的新型身份认证系统。
