利用跨模态数据增强的方法解决红外数据的稀缺问题,进而提升红外人脸识别模型的性能的方法
基于CycleGAN的改进模型(加入MSELoss)进行跨模态的图像合成(即可见光生成红外) 详细介绍见CycleGAN目录
基于Facenet使用VggFace2预训练权重完成红外人脸识别模型再训练
红外人脸识别模型评估,完成对SpeakingFaces中划分的测试集进行测试评估
相应数据地址在各目录下可见
利用跨模态数据增强的方法解决红外数据的稀缺问题,进而提升红外人脸识别模型的性能的方法
基于CycleGAN的改进模型(加入MSELoss)进行跨模态的图像合成(即可见光生成红外) 详细介绍见CycleGAN目录
基于Facenet使用VggFace2预训练权重完成红外人脸识别模型再训练
红外人脸识别模型评估,完成对SpeakingFaces中划分的测试集进行测试评估
相应数据地址在各目录下可见
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