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This paper presents a novel, autonomous learning system working in real-time for face
recognition. Multiple convolutional neural networks for face recognition tasks are available; however,
these networks need training data and a relatively long training process as the training speed depends
on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding
face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to
encode face images from a camera and the Multinomial Naïve Bayes for autonomous training in
the real-time classification of persons. Faces of several persons visible in a camera are tracked using
special cognitive tracking agents who deal with machine learning models. After a face in a new
position of the frame appears (in a place where there was no face in the previous frames), the system
checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is
unknown, the system automatically starts training. As a result of the conducted experiments, one can
conclude that good conditions provide assurance that the system can learn the faces of a new person
who appears in the frame correctly. Based on our research, we can conclude that the critical element
of this system working is the novelty detection algorithm. If false novelty detection works, the system
can assign two or more different identities or classify a new person into one of the existing groups
