I am working on a project based on the facial recognition and verification. I am using Siamese network to get the 128 vector of the face ( Embeddings ).
I am storing the encodings/embeddings of the person's face in the database and then checking or say matching the incoming face's encodings with the previously stored encodings to recognize the person.
To make a robust system, I have to store more than one encodings of the same person. When I have used only a single encoding vector, and matched with :
From face_recognition library (to get distance):
face_recognition.compare_faces( stored_list_of_encodings, checking_image_encodings )
That doesn't work all the time because I have only compared with a single encoding. To make a system sufficient for most cases, I want to store minimum 3 encodings of a same person and then compare with the new data.
Now the question: How to store multiple embeddings of a same person and then compare the distance?
I am using face_recognition as the library and Siamese Network for feature extraction.
Have you considered using an SVM classifier to classify the faces? So the input to the SVM classifier would be the vector of size 128. You can then compile a few of the vectors belonging to the face of a single person (3 in your case) and fit it to an SVM as a class. You can then do the same for different faces (classes).
Then, when predicting a face, simply feed in the new vector and run
svm.predict([..])
I had a similar use-case for my project, but I was using Facenet instead as the feature extractor. Works perfectly.