Facial expressions are crucial in human communication, and their analysis plays a significant role in various applications such as healthcare, security, and human-computer interaction. The primary challenge addressed in this work is the significant drop in accuracy and efficiency of existing facial expression recognition methods when transitioning from controlled to uncontrolled environments. Traditional approaches that perform well under controlled conditions struggle in uncontrolled environment due to increased noise, variability in lighting, and other environmental factors.
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