基于耳脸和轮廓脸融合的多模态识别

Xiaona Xu, Zhichun Mu
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引用次数: 26

摘要

事实证明,人耳识别是一种很有前途的新型认证技术。由于耳朵特殊的生理结构和位置,在无法获得正面人脸图像的情况下,将耳朵与侧面人脸相结合进行识别是合理的。提出了一种基于人耳和轮廓脸的非侵入式多模态识别技术。首先,只捕获人脸侧面视图图像进行识别。然后建立了基于全空间线性判别分析(FSIDA)的耳朵分类器和轮廓人脸分类器。最后,根据贝叶斯理论,采用Product、Sum和中值规则的组合方法对耳朵和轮廓面进行决策融合,并提出了一种改进的两分类器投票规则。实验结果表明,该方法的识别率高于采用单一特征的识别方法,识别范围也大于采用两种单一特征的识别方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Recognition Based on Fusion of Ear and Profile Face
Ear recognition is proved to be a new and promising authentication technique. Because of ear's special physiological structure and location, it is reasonable to combine ear with profile face for recognition in such scenarios as frontal face images are not available. In this paper, a novel non-intrusive multimodal recognition technology based on ear and profile face is proposed. First, only the face profile- view images are captured for recognition. Then ear classifier and profile face classifier based on Full- Space linear Discriminant Analysis (FSIDA) are set up. Finally decision fusion of ear and profile face is carried out using the combination methods of Product, Sum and Median rules according to the Bayesian theory and a modified Vote rule for two classifiers is presented. The results of experiment show that the recognition rate is higher than that of the recognition adopting the single feature, and that the recognition range is larger than that of both uni-modality.
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