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One possible and simple approach in your case is the one-shot recognition. The idea is to use the pre-trained library that extracts the feature vector from the face images. So, you compile a DB with the unique vectors for each of your client faces. These vectors are in Euclidian space, meaning that you can simply calculate the distance between the images, ...


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