Open jinhuang415 opened 5 years ago
Yes your assumption is correct, the author of dlib (the project where the model is from) also states somewhere in his blog I think, that there is a bias towards western faces. I think you can retrain the model using the corresponding dlib example, but afterwards the model weights have to be converted to a the format that tensorflow uses. But I can help you with the latter one, if you really want to retrain the model.
Other than that, I am planning to train an own face recognition model as well since I want to have a more web friendly and more efficient model for face-api.js. But I can't make any promises when that model will be included.
Yes your assumption is correct, the author of dlib (the project where the model is from) also states somewhere in his blog I think, that there is a bias towards western faces. I think you can retrain the model using the corresponding dlib example, but afterwards the model weights have to be converted to a the format that tensorflow uses. But I can help you with the latter one, if you really want to retrain the model.
Other than that, I am planning to train an own face recognition model as well since I want to have a more web friendly and more efficient model for face-api.js. But I can't make any promises when that model will be included.
My project also encountered this problem and I had to use a traditional C/C++ face API. So I'm really looking forward to have this feature in the future. Also please please consider to bring liveness detection to face-api, so I can make sure it is a living person standing in front of a cam not a picture.
@dr1llc4t , hi, I am also interested in retrain the model with a new dataset, would you mind telling me how far have you gone? Or in which direction have you been working on? my email address is sxtgwzz@163.com. ( I don't know what language should I use here, so I just use the common one )
Thanks!
We tried with our company employees (Asian faces) but sometimes the distance between 2 different faces are quite low (below 0.3), we can easily separate them with eyes but looks the face-api pre-trained model could not separate them apart, we tried some western faces and it can work well. So I am thinking if there are not so many Asian face samples in the training dataset so it may not perform very well towards them? If I have some Asian face dataset and want to train a new model, would you please advice how should I do the retrain? Thanks.