使用 IBM Watson Visual Recognition 进行人脸识别
Face Recognition by using IBM Watson Visual Recognition
我目前正在评估 IBM Watson Visual Recognition 服务识别人脸的能力。因此，该系统应该识别我们训练过的每个人。个人可能会穿着不同的衣服，以及其他可能的变化。但是系统应该通过查看每张脸来识别每个人。
按照IBM的说法，IBM视觉识别不支持人脸识别，只支持人脸检测。
Face Recognition: Visual Recognition is capable of face detection
(detecting the presence of faces) not face recognition (identifying
individuals).
我们可以通过为每个人添加不同类型的图像来使用自定义分类器吗？
从开发人员那里获得至少 90% 的准确率的重要 pre/post-work 是什么？
Matt Hill 在 dW Answers 上发布了对 this similar question 的精彩回复。这是他不得不说的：
It is possible to train a custom classifier to try to identify people's faces. It might help to use the face detection service as a preprocessor to give you bounding boxes around faces, and use them to crop the images submitted for custom classification. However, the VR custom learning engine is not optimized for face identification, and I would not expect the results to be as accurate as a system that is designed specifically for face recognition.
The issue is that human faces are typically very similar to each other in with respect to the wide set of features that were trained in learning the basis of the system, which needed a very broad exposure to many types of scenes and objects.
我目前正在评估 IBM Watson Visual Recognition 服务识别人脸的能力。因此，该系统应该识别我们训练过的每个人。个人可能会穿着不同的衣服，以及其他可能的变化。但是系统应该通过查看每张脸来识别每个人。
按照IBM的说法，IBM视觉识别不支持人脸识别，只支持人脸检测。
Face Recognition: Visual Recognition is capable of face detection (detecting the presence of faces) not face recognition (identifying individuals).
我们可以通过为每个人添加不同类型的图像来使用自定义分类器吗？
从开发人员那里获得至少 90% 的准确率的重要 pre/post-work 是什么？
Matt Hill 在 dW Answers 上发布了对 this similar question 的精彩回复。这是他不得不说的：
It is possible to train a custom classifier to try to identify people's faces. It might help to use the face detection service as a preprocessor to give you bounding boxes around faces, and use them to crop the images submitted for custom classification. However, the VR custom learning engine is not optimized for face identification, and I would not expect the results to be as accurate as a system that is designed specifically for face recognition.
The issue is that human faces are typically very similar to each other in with respect to the wide set of features that were trained in learning the basis of the system, which needed a very broad exposure to many types of scenes and objects.