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Facial expressions are the basic input for visual emotion detection. They are of great importance in computer vision society. In last decade, substantial amount of work has been done in the field of facial expressions datasets. This survey covers all of the publically available databases in detail and provides necessary information about these sets. This review delivers comprehensive support to researchers in selection of their desired dataset. The datasets are organized in decreasing order of their importance with respect to diversity in expressions, poses, number of images and resolution. This survey also provides comprehensive tabular comparison of different face based databases.

Automated detection and tracking of a person’s actions plays a vital role in surveillance systems. Human activity detection has been carried out by using a variety of features; including flow-based, spatio-temporal and interest points based. We have created a fusion of features by incorporating those which give better results. LBP, HOG, Haar wavelets, SIFT, velocity and displacement being the major ones. By employing the time efficiency and optimality of SMO to train SVM, we have trained our system for both single person and multi-human action classification with improved accuracy. A generalized hierarchy of actions has been presented in this paper to demonstrate the extension of our methodology. We have achieved an accuracy of 91.99% on combination of KTH and Weizmann dataset and 86.48% on multi-human dataset. We have introduced our self-generated multi-human activity dataset in the following paper.