Comments (5)
我已经修改了get_face_feature.py和detect_face_align_rec.py里的程序逻辑,您可以同步更新一下
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出现这个错误,你的faces_feature应该是只包含有1个人脸的特征向量的。做人脸识别,通常是需要提取多个人脸的特征向量,然后保存到faces_feature矩阵里的。看get_face_feature.py里第99行代码:
face_feature = (np.squeeze(np.asarray(feature_list)), name_list)
这里之所以使用了np.squeeze去消除维度是1的维,是因为在arcface输出的特征量是1x512的,如果提取10个人脸特征向量,那么就得到10x1x512的矩阵,显然1是多余的,因此就是用np.squeeze消除它。而如果只提取了一个人脸特征向量,那么得到1x1x512的矩阵,这是使用np.squeeze会把人脸个数1这个维度信息也消除掉,得到长度是512的一维数组,那么在后面运行detect_face_align_rec.py时,就会出现上面的错误:XA必须是2维数组
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我想通过两张人脸欧拉距离得到人脸相似度,要怎么做呢,翻了很久资料没找到答案。个人感觉目前这个arcface的特征值比对结果也不太理想,有没有更好的选择。
from 10kinds-light-face-detector-align-recognition.
arcface建议重新训练,我用的是arcface已开源的训练模型
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Related Issues (20)
- 关于准确率评估
- 关于PFLD算法 HOT 1
- opencv版本问题
- Model Files on some other drive
- OMP: Error
- 检测
- 模型文件
- GPU版本完全没法跑啊,一个接一个的错误 HOT 4
- 有借鉴意义
- 哪个最快
- 您好,最近的scrfd运行不了,会报错,是代码的问题。您可以检查一下吗
- 你好,想请教下RetinafaceDetector_dnn这个函数在哪里? HOT 1
- libfacedetect_align_module有问题,输出的向量尺寸错误 HOT 4
- retina_face 的模型是从哪里获取的?用哪个数据训练的?
- retinaface_detect 与retinaface_dnn_detect区别 HOT 2
- 视频人脸识别 HOT 1
- Run_all_compare_time.py 里如果用GPU跑报错 HOT 4
- 当图像尺度较小时,facebox报错 HOT 1
- 使用mtcnn_pfld_landmark运行getfeature时报错 HOT 2
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