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.
目前关于人耳识别的研究多关注于特征的提取。
Currently, most researches are focused on characteristics of human ear.
本文提出了一种基于灰度曲面匹配的人耳识别方法。
An ear recognition method based on gray surface matching has been proposed.
人耳图像的去噪是人耳识别过程中的一个重要步骤。
Ear image denoising plays an important role in ear recognition system.
比较了三种用于人耳识别的局部表征方法的识别准确率。
Three different localized representation methods to face recognition are compared in terms of recognition accuracy.
提出了一种基于PIDC和二叉决策树s VM的人耳识别方法。
In this paper, we present an ear recognition method using PIDC and binary tree SVM classification.
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.
在实际的人耳识别系统中，人耳的准确定位是影响识别率的一个重要因素。
Ear detection is an important step in ear recognition and the exact localization for ear directly affects the final recognition rate.
人耳图像的归一化在人耳识别中具有相当重要的意义，为后续的工作提供了前提。
Normalization of ear image has a very important significance in ear recognition, and provides precondition for later work.
目前，在国内和国外，人耳识别尚处于起步阶段，一些基础性问题还没有得到解决。
At the present, ear recognition is underway in domestic and abroad, so there are many basic problems that are not solved.
在人耳识别的最后部分尝试探索使用在小样本识别中具有很大优势的支持向量机方法。
On the last part of ear recognition, the author attempts to employs the method of SVM (Support Vector Machine), which has some superiority in small sample biometric recognition.
人耳识别可以作为其他生物识别技术的有益补充，也可以单独应用于一些个体识别场合。
Ear recognition can be a beneficial supplement for other biologic recognition or be solely used in certain situation.
一个完整的人耳识别系统主要包括以下几个部分：图像读取、人耳检测、特征提取和识别。
A whole ear recognition system is mainly composed of four steps: image inputting, ear detection, feature extraction and recognition.
人耳识别作为生物识别一个新的研究方向，由于其独特的生理特征越来越受到更多人的关注。
As a novel biometric identification technology, more and more attention was paid to the recognition of human ear because of its unique physiological characteristics.
随着人耳识别研究的深入，人耳检测作为人耳识别系统中关键的一步也开始引起人们的重视。
Now, with in-depth study of recognition system, ear detection in different conditions becomes more and more important as the first key step in the whole recognition system.
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.
虽然关于人耳识别的算法已有不少，但是利用最佳边缘信息进行人耳识还是一个未深入研究的方向。
There are many algorithms about ear recognition now, but ear recognition employing the spatial information of edge map associated with optimal information remains an unexplored direction.
人耳识别是一种新的生物特征识别技术，目前，研究尚处于起步阶段，有关的理论和方法还很不完善。
Ear recognition is a new technique of biological recognition and the research on ear recognition is still in the preliminary stage currently. The mature theory or method has not been found yet.
人耳识别作为新的生物特征识别技术，首先要解决作为基础的边缘检测和特征提取等图像处理方面的问题。
As a novel biometric authentication technology, human ear recognition needs to solve the problems of image processing such as edge detection and feature extraction.
人耳识别技术是一种新的生物特征鉴别技术，以其独特的应用方向和优势已经引起了研究者越来越多的注意。
Ear recognition is a new technology of biologic recognition, which has drawn more and more attention from the scientists because of its unique feature and applied direction.
人耳的角度变化和遮挡是人耳识别中的难点问题，SIFT局部描述算子具有对图像尺度缩放、平移、旋转等的不变性，因此提出利用SIFT特征的人耳识别算法。
The variety of ear angle and occlusion are the difficulties of ear recognition. The Scale Invariant Feature Transform(SIFT) is invariant to image scaling, translation and rotation.
人耳的角度变化和遮挡是人耳识别中的难点问题，SIFT局部描述算子具有对图像尺度缩放、平移、旋转等的不变性，因此提出利用SIFT特征的人耳识别算法。
The variety of ear angle and occlusion are the difficulties of ear recognition. The Scale Invariant Feature Transform(SIFT) is invariant to image scaling, translation and rotation.
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