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all unknown even in an abvious case #6
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@smajida So sorry for delayed response and thank you for trying my code, you should put aligned_face in data/facedb/aligned_face.jpg, you can obtain through #7 as for the recognition result, i just use the trained sphereface model from author, but use myself modified mtcnn align method, so it will have some bad affect, eventhough you can adjust the score threshold in FaceClassifier, but i think it's not a ultimate solution. |
@tpys thanks for your reply. no problem. If you notice my first message, I have already tried putting aligned face in the mentioned folder and I got the video results which I have put its link above. As you can see, not even a single time Messi is recogized which means zero accuracy in true positive. I am surprized because the main author of sphereface has put a video demo in his repository in which it is almost 100% accurate both in true positive and true negative. What could be the reason? |
Hi@smajida, I have the same question, did you find the solution? |
@smajida @anguoyang I don't exactly why? maybe the pre-trained model doesn't work well with my c++ mtcnn alignment, currently i am busy on a face alignment algorithm, when i got time, will retrain the sphereface model. if you figure it out, please let me known too, thanks guys. |
Hi@tpys, I have checked the recognition part, it seems that your source code is different with the original matlab one: I will try to convert the original matlab source into c++ and have a try on their original model, and then get back here. |
Hi @anguoyang @tpys , perhaps author of Sphereface algorithm has put the video demo which was processed with Sphereface64 rather than 20 model. Is Sphereface64 pre-trained model or its script available? I can give it try that and report the output here. I tried to convert the matlab code to C++, but I have not made it. If I make it, I will definitely post it here so others can benefit from that. :) |
@smajida @anguoyang definitely worth try sphereface64 model, but in the recognition part, i basic follow same principle, subtract mean, divide by std, flip input, concatenate output to form 1024dim features, then normalize the feature.
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I have found the reason, look here #8. |
@macheski I have asked the original Author to provide the video script long ago, but he has not replied to this issue. I am wondering how one can the similar result. |
@macheski string filename = video_dir + "/friends.mp4";
cout << "Initialization of reading video!" << endl;
VideoCapture cap(filename);
here is the way I put the images in facedb folder: facedb-- here is the results: first Joey is recognized but Ross is also recognized as Joey. afterwards, all of them including Joey is not recognized. It seems the first person in the folder of images is recognized and then this recognition stays valid for some more recognitions and then it is totally deleted. There is many false face detection. As far as I have seen. MTCNN is very good in detection. So why detection quality is not good here?! can you test the code on the following video: I investigated the outputs which you can see below. It seems after the system recognizes Joey it does not recognition for a few frames. I0119 13:23:09.173434 46126 net.cpp:255] Network initialization done. |
@anguoyang @macheski @tpys BTW, Chanlder's color is mistakenly white which is similar to color of unknown class. I have to try to rotate bounding boxes like the original author's video demo. |
@macheski |
Hi,
@tpys
I tried a sample video with one sample image. However, the recognition is so bad.
This is the sample image which I have put in facedb folder:
I also tried with its aligned image as well:
and this is the ouput:
https://www.youtube.com/watch?v=2HYxFLRBMqM&feature=youtu.be
I have left configuration as the default.
I put messi.jpg in folder messi in facedb. only the first parts of the paths were modified in all_in_one code.
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