I am trying to create a script that is able to evaluate a model on lfw dataset. As a process, I am reading pair of images (using the LFW annotation list), track and crop the face, align it and pass it through a pre-trained facenet model (.pb using tensorflow) and extract the features. The feature vector size = (1,128) and the input image is (160,160).
To evaluate for the verification task, I am using a Siamese architecture. That is, I am passing a pair of images (same or different person) from two identical models ([2 x facenet] , this is equivalent like passing a batch of images with size 2 from a single network) and calculating the euclidean distance of the embeddings. Finally, I am training a linear SVM classifier to extract 0 when the embedding distance is small and 1 otherwise using pair labels. This way I am trying to learn a threshold to be used while testing.
Using this architecture I am getting a score of 60% maximum. On the other hand, using the same architecture on other models (e.g vgg-face), where the features are 4096 [fc7:0] (not embeddings) I am getting 90%. I definitely cannot replicate the scores that I see online (99.x%), but using the embeddings the score is very low. Is there something wrong with the pipeline in general ?? How can I evaluate the embeddings for verification?
Nevermind, the approach is correct, facenet model that is available online is poorly trained and that is the reason for the poor score. Since this model is trained on another dataset and not the original one that is described in the paper (obviously), verification score will be less than expected. However, if you set a constant threshold to the desired value you can probably increase true positives but by sacrificing f1 score.