人耳识别

近年来又兴起了一种新的生物特征识别技术-人耳识别。

Recently , ear recognition becomes a newly emerging biometrics trend .

基于小波变换和正交质心算法的人耳识别研究

Ear recognition based on wavelet transform and orthogonal centroids algorithm

基于小波变换和LDA/FKT及SVM的人耳识别

Human ear recognition based on wavelet transform , LDA / FKT and SVM

提出了一种独立分量分析和BP神经网络相结合的人耳识别新方法（ICABP法）。

A new ear recognition method combining independent component analysis ( ICA ) and BP neural network was proposed .

基于ICA的非线性自适应特征融合的人耳识别

An ICA-Based Ear Recognition Method through Nonlinear Adaptive Feature Fusion

基于二维Fisher线性判别的人耳识别

Ear Recognition Based on Two-dimensional Fisher Linear Discriminant

基于多特征融合和Bagging神经网络的人耳识别

Ear recognition based on feature fusion and Bagging neural network

基于ICA和KPCA人耳识别技术比较

The Comparison of Human Ear Recognition Technology based on ICA and KPCA

基于PIDC和二叉决策树SVM的人耳识别

Ear Recognition Based on PIDC and Binary Tree SVM Classification

这些特性使得人耳识别成为一种有前景的新技术。当前国内外人耳识别工作主要集中在2D人耳正视图及3D人耳距离图像（Rangeimage）识别。

All these merits make ear biometrics be a new promising technology . Currently , ear recognition works in oversea and domestic concentrated on the ways of 2D front view image and 3D range image .

提出了一种基于PIDC和二叉决策树SVM的人耳识别方法。

In this paper , we present an ear recognition method using PIDC and binary tree SVM classification .

利用该方法对400个人耳进行识别实验，并将识别结果同PCA方法进行了比较，实验表明，文中方法降低了分类难度，提高了人耳识别率。

At last , we compare the experiment results between our method and PCA using 400 ear samples , the results show that our method can reduce the difficulties of calculation and get high recognition rate .

用于多姿态人耳识别的局部线性嵌入及其改进算法

Locally Linear Embedding and Its Improved Algorithm for Multi-Pose Ear Recognition

提出了一种多特征信息融合的人耳识别方法。

A new ear recognition method based on feature fusion was presented .

人耳识别是生物特征识别中的一种新兴技术。

Ear recognition is a new technology of biologic recognition .

声音（即使是频率超出人耳识别范围的声音也行）被送入水中，水就会相应地震动。

Sound-even at frequencies humans can 't hear-is directed at the water .

基于小波分解和鉴别共同矢量的人耳识别

Ear Recognition Based on Wavelet Decomposition and Discriminative Common Vector

基于力场收敛特征的多姿态人耳识别

Multi-pose ear recognition based on force field convergence feature

对人耳识别中若干关键问题进行了研究。

Some key issues in ear recognition were investigated .

局部子空间映射在人耳识别中的应用

Application of localized subspace projections on ear recognition

基于KDA/GSVD和支持向量机的人耳识别

Ear Recognition Using KDA / GSVD and SVM

基于复合结构分类器的人耳识别

Ear recognition based on compound structure classifier

人耳识别技术作为一种新的研究在生物特征识别领域提出一种新思路。

Research of ear recognition technology creates a new way in the field of biometrics recognition .

目前人耳识别技术在国内外尚处于初步探索研究阶段，还没有形成较为完善的理论体系。

Since ear recognition is in the primary research , there is not sufficient theory about it .

同时，人耳识别可以一种非入侵的方式进行，易于接受。

At the same time , ear recognition can work in a non-invasive way which is easily accepted .

最初的人耳识别研究主要集中在人耳特征的提取及识别领域，对人耳检测尚未有深入的研究。

Compared to ear-feature extraction and recognition , little attention was paid to ear detection in the early research .

目前大多数人耳识别的研究工作都是在假定图像中的人耳己经被检测和定位的前提下进行的。

Most research on ear identification is implemented on the assumption that the ear has already been detected and located .

目前人耳识别正处于起步阶段，方法并不成熟，识别率不高。

Now ear recognition just gets off the mark , and its feature extraction method and recognition rate is not satisfactory .

本文介绍了整个系统从图像采集到人耳识别再到识别后处理控制（硬件控制）的详细设计过程。

The procedure from image acquisition through ear recognition to the post process ( hardware control ) is introduced in this thesis .

人耳识别既可以单独应用于一些个体识别场合，也可以作为其它生物特征识别技术的有益补充。

Ear recognition is not only a beneficial supplement for other biometrics technology , but also can be solely used at some identification occasion .