Creation of an application for real time emotions recognition on a low resource machinies
Abstract
The improvements related to construction of better algorithms and big data availability gave a possibility to solve a huge number of vision related tasks. One of such problems is understanding and detection of facial expressions. This task is crucial for many applications including market research, making self-driving cars safer, analysis of interviews, etc. The described task is not a new one, thus many solutions exist along with datasets, including FER [1] and AffectNet [2]. However, the task becomes more challenging if we consider detecting a new class of images, the balance between accuracy, latency and size of the model, hyperparameters tuning. The aim of this work is to give exhaustive answers to all the aforementioned questions via description of a successful application serving a neural network trained on a custom dataset for emotion detection.
References
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