Deep Cross Polarimetric Thermal-to-Visible Face Recognition

S. M. Iranmanesh, Ali Dabouei, Hadi Kazemi, N. Nasrabadi
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引用次数: 46

Abstract

In this paper, we present a deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. Polarization state information of thermal faces provides the missing textural and geometrics details in the thermal face imagery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The proposed architecture is able to make full use of the polarimetric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recognition methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embedding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superiority of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms.
深交叉极化热-可见人脸识别
在本文中,我们提出了一个深度耦合学习框架来解决极化热人脸照片与可见人脸库的匹配问题。热面偏振态信息提供了热面图像中存在于可见光谱中缺失的纹理和几何细节。我们提出了一个耦合的深度神经网络架构,利用相对较大的可见和热数据集来克服过拟合问题,最终我们通过极化热人脸数据集来训练它,这是同类数据集中的第一个。与传统的浅层热-可见人脸识别方法相比,该架构能够充分利用极化热信息来训练深度模型。所提出的耦合深度神经网络还在非线性嵌入空间中寻找全局判别特征,将极化热面与其对应的可见面联系起来。结果表明,与最先进的交叉热-可见人脸识别算法模型相比,我们的方法具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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