Keras-VGGFace2-ResNet50 | This repo contains a Keras implementation of the paper | Machine Learning library

The aim of my project is extracting specific facial features on mobile phone. This a verification application using user's face. Given two different images of the same person, extracting the features as close as possible.

Right now, I use the pretrained model and weights of VGGFace team as a feature extractor, you can download the model in here. However, when I extracted features based on the model, the result was not good enough, I described what I did and what I want as below:

I extract features from Emma Watson' images, image_1 returns feature_1, image2 returns feature_2 and so on (vector length = 2048). If feature[i] > 0.0, convert it to 1.

for i in range(0, 2048): if feature1[0][i] > 0.0: feature1[0][i] = 1

Then, I compare the two features vector using Hamming distance. Hamming distance is just a naive way to compare, in real project, I will quantize those features before comparing. However, the distance between 2 images of Emma still large even though I use 2 neural facial expression images (same emotion, different emotion type return worse result).

My question is how could I train the model to extract features of target user. Imaging, Emma is a target user, and her phone only need to extract her features. When someone try to unlock Emma's phone, her phone extract this person's face then compare with saved Emma's features. In addition, I don't want to train a model to classify 2 classes Emma and not Emma. The thing I need is comparing extracted features.

To sum up, If we compare features from different images of the same person, the distance (differences) should be "close" (small). If we compare features from different images of different people, the distance should be "far" (large).

Thank you so much.