微笑检测（除了 OpenCV 之外还有其他选择吗？）
是否有任何库可以替代 OpenCV 来检测微笑。
我不想使用 OpenCV，因为它有时由于背景而无法检测人脸。
有人知道其他图书馆吗？除了 OpenCV 之外？
Is there any library alternative to OpenCV which detects smile.
I dont want to use OpenCV as it sometimes fails to detect faces due to background.
Any one knw other library ? other than OpenCV ?
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（4）
我建议您查看机器感知工具箱 (MPT 库）。
我有机会在 Openframeworks Goldsmiths 的 OpenCV 研讨会 并且有一个 c++ 微笑检测示例可用。
我想你可以尝试 iPhone with openframeworks 的 MPT 库，或者直接从 iphone 链接到该库项目。
理想的照明设置将保证更好的结果，但考虑到您想在移动设备上使用它，您必须告知用户微笑检测在极端条件下（不良照明）可能会失败
HTH
I would recommend having a look at The Machine Perception Toolbox (MPT Library).
I had a chance to play with it a bit at an Openframeworks OpenCV workshop at Goldsmiths and there is a c++ smile detection sample available.
I imagine you can try the MPT Library for iPhone with openframeworks or simply link to the library from an iphone project.
An ideal lighting setup will guarantee better results, but given that you want to use this on a mobile device, you must inform your users that smile detection might fail under extreme conditions (bad lighting)
HTH
您如何进行微笑检测？我在默认的 OpenCV 人脸检测级联中看不到特定于微笑的 Haar 数据集。我怀疑你的问题是训练数据而不是 OpenCV 本身。
How are you doing smile detection? I can't see a smile-specific Haar dataset in the default OpenCV face detection cascades. I suspect your problem is training data rather than OpenCV itself.
如果您需要一个可以运行的应用程序，那么 Egawer 是一个很好的起点。
https://github.com/Atrac613/egawer-iOS
我查看了 smileD_haarcascade_v0 的训练图像。 05、发现它们包括全脸。因此，它似乎是一个“笑脸”检测器，而不仅仅是一个微笑检测器。虽然这看起来更容易，但也可能不太准确。
最好的方法是创建您自己的 Haar Cascade XML 文件，但不可否认的是，我们大多数开发人员没有时间这样做。您可以通过均衡图像的亮度来显着改善结果。
Egawer is a good starting point if you need a working app to begin with.
https://github.com/Atrac613/egawer-iOS
I checked the training images of smileD_haarcascade_v0.05, an found that they include the full face. So, it seems to be a "smiling face" detector rather than a smile detector alone. While this seems easier, it can also be less accurate.
The best is to create your own Haar Cascade XML file, but admittedly most of us developers don't have time for that. You can improve the results considerably by equalizing the brightness of the image.
iOS 7 现在在 CoreImage 中原生支持明喻检测。这是 API 差异：
iOS 7 now has native support of simile detection in CoreImage. Here is the API diff: