FACIAL EXPRESSION RECOGNITION
Abstract
Facial expression recognition (FER) plays a crucial role in various applications, including
human-computer interaction, emotion analysis, and security systems. This developed and
implement a facial expression recognition system using computer vision techniques and the
DeepFace library. The objectives include designing, implementing, and evaluating the
performance of the system. The DeepFace framework was utilized to analyze facial
expressions, identifying emotions such as happiness, sadness, anger, fear, surprise, disgust,
and neutrality. Experimental results produced two primary outputs. In the first output, the
dominant emotion detected was "happy" with a confidence level of 99.98%, while in the
second output, the dominant emotion was "neutral" with a confidence level of 99.79%. These
findings demonstrate the system's effectiveness in recognizing facial expressions with high
accuracy. The study highlights the potential of DeepFace in emotion detection and its
applicability in real-world scenarios.
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