摘要翻译：
耳部生物特征被认为是与虹膜、指纹特征一致的最可靠、最不变的生物特征之一。在许多情况下，耳朵的生物特征可以与人脸的生物特征相比较，涉及到许多生理和纹理特征。本文提出了一种鲁棒高效的人耳识别系统，该系统采用尺度不变特征变换(SIFT)作为特征描述子，对人耳图像进行结构化表示。为了使其对用户认证具有更强的鲁棒性，只考虑在一定范围内具有颜色概率的区域进行不变SIFT特征提取，其中K-L散度用于保持颜色一致性。采用混合高斯模型和向量量化方法对人耳颜色模式进行聚类，形成人耳肤色模型。最后，将K-L散度应用到GMM框架中，通过比较一对参考模型和探针耳图像之间的颜色相似性来记录指定范围内的颜色相似性。在对部分彩色切片区域的耳图像进行分割后，提取SIFT关键点，并对提取的SIFT特征创建增广向量进行匹配，以实现对参考模型和探针耳图像之间的匹配。该方法在IITK人耳数据库上进行了测试，实验结果表明，在从彩色切片区域提取不变特征的同时，提高了识别精度，保持了系统的鲁棒性。
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英文标题：
《SIFT-based Ear Recognition by Fusion of Detected Keypoints from Color
  Similarity Slice Regions》
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作者：
Dakshina Ranjan Kisku, Hunny Mehrotra, Phalguni Gupta, and Jamuna
  Kanta Sing
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT) as feature descriptor for structural representation of ear images. In order to make it more robust to user authentication, only the regions having color probabilities in a certain ranges are considered for invariant SIFT feature extraction, where the K-L divergence is used for keeping color consistency. Ear skin color model is formed by Gaussian mixture model and clustering the ear color pattern using vector quantization. Finally, K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference model and probe ear images. After segmentation of ear images in some color slice regions, SIFT keypoints are extracted and an augmented vector of extracted SIFT features are created for matching, which is accomplished between a pair of reference model and probe ear images. The proposed technique has been tested on the IITK Ear database and the experimental results show improvements in recognition accuracy while invariant features are extracted from color slice regions to maintain the robustness of the system.
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PDF链接：
https://arxiv.org/pdf/1002.0412