Abstract
Video surveillance, within prisons, monitor the emotional status of inmates, as human emotions provide insight into their intended actions. This work attempts to build an automated system that cognizes human emotion from the pattern of pixels in a facial image. In this paper, a solution based on Iterative Optimization Strategy is proposed to minimize the loss function. The proposed strategy is applied in the Fully Connected layer of Deep ConvNet. To evaluate the performance of the system we use two benchmark datasets named Japanese Female Facial Expression database and Kaggle Facial Expression Recognition dataset respectively. The system was manually tested with captured video, and video from a real documentary on YouTube. From the results, we could see that the proffered system achieves a precision, i.e. (the closeness of agreement among a set of results) of 0.93. Abbreviations: DCNN: Deep Convolutional Neural Network; ER: Error; LR: Learning Rate;FM: Feature Map
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