Illumination Invariant Thermal Face Recognition using Convolutional Neural Network

Shinfeng D. Lin, Kuanyuan Chen
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引用次数: 2

Abstract

An illumination invariant thermal face recognition using convolutional neural network is proposed. The proposed CNN model includes training phase and testing phase. The convolutional layer of CNN model is utilized to extract features. This produces a feature map in the output image and the feature maps are fed to the next layer. The output from the last pooling layer is flattened and fed into a fully connected layer (FC layer). The goal of FC layer is to employ these features for classifying the input image into various classes based on the training datasets. Compared with the traditional thermal face recognition, experimental results demonstrate the superiority of the proposed method.
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基于卷积神经网络的光照不变热人脸识别
提出了一种基于卷积神经网络的光照不变热人脸识别方法。提出的CNN模型包括训练阶段和测试阶段。利用CNN模型的卷积层提取特征。这在输出图像中产生一个特征映射,并将特征映射馈送到下一层。最后一个池化层的输出被平面化并馈送到一个完全连接的层(FC层)。FC层的目标是利用这些特征根据训练数据集将输入图像分类成不同的类。与传统的热人脸识别方法相比,实验结果证明了该方法的优越性。
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