Smart cities include good infrastructure, better transportation, connectivity, security, etc. Human identification is a part of providing security to societies. Artificial intelligence plays an important role in this objective. In this paper, we have presented a state-of-the-art on different machine learning techniques used for the identification of human beings according to their movement. We found that there are various machine learning techniques, viz., support vector machine (SVM), k-nearest neighbor (k-NN), convolutional neural network (CNN), Fuzzy set theory, Discrete Fourier transform which are used for human authentication. We presented a comparative study of such schemes and provided our major findings of the survey. We observed that under certain conditions, neural network-based techniques are performing better than the other existing schemes of gait-based human authentication.

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