Although an old topic of exploration, facial expression has become an important field of research. Marketing and medical research frequently use emotion analysis to identify customer needs and emotions. The present paper deals with facial expression, but it is about expression-invariant face recognition rather than emotion detection. The research addresses the need to supplement the training set with synthesized images of possibly missing facial expressions to augment the accuracy of the face recognition task. Moreover, the paper integrates straightforward techniques with generally acknowledged methods to demonstrate that realistic facial expressions can be generated from only one face image. This represents an important contribution of the work. Six basic expressions (anger, disgust, fear, happiness, sadness, and surprise) with five intensity levels are assumed.
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