Face recognition with a small number of samples

Question

Can anyone advise me way to build effective face classifier that may be able to classify many different faces (~1000)?

And i have only 1-5 examples of each face

I know about opencv face classifier, but it works bad for my task (many classes, a few samples). It works alright for one face classification with small number of samples. But i think that 1k separate classifier is not good idea

I read a few articles about face recognition but methods from these articles reqiues a lot of samples of each class for work

PS Sorry for my writing mistakes. English in not my native language.

Answer 1

Actually, for giving you a proper answer, I'd be happy to know some details of your task and your data. Face Recognition is a non-trivial problem and there is no general solution for all sorts of image acquisition.

First of all, you should define how many sources of variation (posing, emotions, illumination, occlusions or time-lapse) you have in your sample and testing sets. Then you should choose an appropriate algorithm and, very importantly, preprocessing steps according to the types.

If you don't have any significant variations, then it is a good idea to consider for a small training set one of the Discrete Orthogonal Moments as a feature extraction method. They have a very strong ability to extract features without redundancy. Some of them (Hahn, Racah moments) can also work in two modes - local and global feature extraction. The topic is relatively new, and there are still few articles about it. Although, they are thought to become a very powerful tool in Image Recognition. They can be computed in near real-time by using recurrence relationships. For more information, have a look here and here.

If the pose of the individuals significantly varies, you may try to perform firstly pose correction by Active Appearance Model.

If there are lots of occlusions (glasses, hats) then using one of the local feature extractors may help.

If there is a significant time lapse between train and probe images, the local features of the faces could change over the age, then it's a good option to try one of the algorithms which use graphs for face representation so as to keep the face topology.

I believe that non of the above are implemented in OpenCV, but for some of them you can find MATLAB implementation.

I'm not native speaker as well, so sorry for the grammar

Answer 2

Coming to your problem , it is very unique in its way. As you said there are only few images per class , the model which we train should either have an awesome architecture which can create better features within an image itself , or there should be an different approach which can achieve this task . I have four things which I can share as of now :

• Do data pre-processing and then create a bigger dataset and train on a neural network ideally. Here, we can do pre-processing like: - image rotation - image shearing - image scaling - image blurring - image stretching - image translation and create atleast 200 images per class. Please checkout opencv documentation which provides many more methods on how you can increase the size of your dataset. Once you do this, then we can apply transfer learning , which is a better approach than training a neural network from scratch.

Transfer learning is a method where we train a network on our own custom classes , and this network is already pre-trained on 1000's of classes. Since our data here is very less, I would prefer transfer learning only. I have written a blog on how you can approach this using tranfer learning after you have the required amount of data. It is linked here. Face recognition also is a classification task itself, where each human is a separate class. So, follow the instructions given in the blog , may be it would help you create your own powerful classifer.

• Another suggestion would be , after creating a dataset , encode them properly. This encoding would help you preserve the features in an image and can help you train better networks. VLAD ,Fisher , Bag of Words are few encoding techniques. You can search few repositories online which have implemented these already on ORL database. Once you encode , train the network on the encodings , you will obviously see a better performance.
• Even do check out , Siamese network here which is meant for this purpose I feel . Here they compare two images with similar characteristics on different networks and there by achieve better classification accuracies . Git repository is here.
• Another standard approach would be using SVM , Random forests since the data is less. If you still prefer neural networks the above methods would serve you the purpose. If you intend to go with encodings , then I would suggest random forests , as it is highly preferrable in learning and flexible too.

Hopefully , this answer would help you proceed in the right direction of achieving things.

Answer 3

You might want to take a look at OpenFace, a Python and Torch implementantion of face recognition with deep neural networks: https://cmusatyalab.github.io/openface/