Image similarity comparison

You can use existing deep learning architectures like VGG to generate features from images and then use a similarity metric like cosine similarity to see if two images are essentially the same.

The whole pipeline is pretty easy to set up and you do not need to understand the neural network architecture (you can just treat it like a black box). Also, these features are pretty generic and can be applied to find similarity between any kind of objects, not just faces.

Here are a couple of blogs that walk you through the process. http://blog.ethanrosenthal.com/2016/12/05/recasketch-keras/ https://erikbern.com/2015/09/24/nearest-neighbor-methods-vector-models-part-1.html

Amazon has a new API called Rekognition which allows you to compare two images for facial similarity. The api returns a similarity percentage for each face with one another and the bounding boxes for each face.

Rekognition also includes an api for both Facial Analysis (returning the gender, approximate age, and other relevant facial details) and Object Scene Detection(returning tags of objects that are within in image).

There is a discussion of image similarity algorithms at stack overflow. Since you don't need to detect warped or flipped images, the histogram approach may be sufficient providing the image crop isn't too severe.