Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition


Automatic have an effect on analysis has attracted nice interest in varied contexts together with the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are many open questions on what the important cues to interpret facial expressions are and a way to encode them. During this paper, we have a tendency to review the progress across a range of affect recognition applications to simplify these fundamental queries. We tend to analyse the state-of-the-art solutions by decomposing their pipelines into basic elements, namely face registration, representation, dimensionality reduction and recognition. We have a tendency to discuss the role of these elements and highlight the models and new trends that are followed in their style. Moreover, we have a tendency to give a comprehensive analysis of facial representations by uncovering their blessings and limitations; we elaborate on the type of knowledge they encode and discuss how they handle the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias. This survey permits us to identify open issues and to outline future directions for planning real-world affect recognition systems.

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