它基于 dlib 开发,并采用深度学习技术构建了最先进的人脸识别模型,在 Labeled Faces in the Wild 数据集上达到 99.38%的准确率。该库提供了简单易用的命令行工具face_recognition,可以对一整个文件夹中的图像进行批量处理。 在照片中找出所有出现过的人脸 找到并标记每张照片里面每个人眼睛、鼻子、嘴巴和下巴等部位 ...
使用世界上最简单的人脸识别库从 Python 或命令行识别和操作人脸。 使用dlib通过深度学习构建的最先进的人脸识别技术构建。该模型在 Wild基准测试中的Labeled Faces上的准确率为 99.38% 。 这还提供了一个简单的face_recognition命令行工具,可让您从命令行对图像文件夹进行人脸识别!
That’s not to say that face recognition systems are poor. Far from it. The best systems can beat human performance in ideal conditions. But their performance drops dramatically as conditions get worse. So computer scientists would dearly love to develop an algorithm that can take the crown in...
使用webface人脸数据集以及DeepID网络,通过Caffe训练出模型参数,得到LFW二分类的人脸识别准确率。 - txqyou/face_recognition
Face recognition using triplet loss, implementing FaceNet with pytorch. A small face dataset which consists of 62 IDs with 20 face images per ID for testing. The algorithm achieves accuracy above 97%. 人脸识别项目,提供一个小型数据集用作验证,使用三元组损失函数提升准确率和泛化能力,对FaceNet进行了...
aCompared to the ORL database, the Yale face database has different illuminations. The experimental setting is the same as that of the ORL database. The comparison results on the two databases are illustrated in Table2. Figure 7shows the recognition accuracy curves versus the variations of the...
Face recognition using triplet loss, implementing FaceNet with pytorch. A small face dataset which consists of 62 IDs with 20 face images per ID for testing. The algorithm achieves accuracy above 97%. 人脸识别项目,提供一个小型数据集用作验证,使用三元组损失函数提升准确率和泛化能力,对FaceNet进行了...