摘要翻译：
本文提出了一种基于高斯混合模型(GMM)的人脸和耳朵生物特征识别系统，该系统利用Dempster-Shafer(DS)决策理论对Gabor响应的估计值进行信念融合。利用Gabor小波滤波器对人脸和人耳图像进行卷积，提取空间增强的Gabor人脸特征和Gabor人耳特征。此外，将GMM分别应用于高维Gabor人脸和Gabor耳响应，进行定量测量。在GMM中采用期望最大化(EM)算法估计密度参数。这产生了两组特征向量，然后使用Dempster-Shafer理论进行融合。实验在包含400个个体的人脸和耳朵图像的多模态数据库上进行。研究发现，利用Gabor小波滤波器结合GMM和DS理论可以提供鲁棒有效的多模态融合策略。
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英文标题：
《Multibiometrics Belief Fusion》
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作者：
Dakshina Ranjan Kisku, Jamuna Kanta Sing, Phalguni Gupta
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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：
  This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face and Gabor ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on multimodal database containing face and ear images of 400 individuals. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.
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PDF链接：
https://arxiv.org/pdf/1002.2755