Abstract
With the rise of artificial intelligence, machine learning (ML) is increasingly integrated into daily life. Facial expressions, lasting about 1/20th of a second and difficult to conceal, convey nuanced emotions beyond words. They are categorized as macro expressions, displayed under normal circumstances, and micro expressions, fleeting and subconscious. However, recognizing expressions in photos is challenging due to varied backgrounds, appearances, age, and race, impacting accuracy. Addressing this, our research focuses on a machine learning-based facial expression recognition algorithm. Our experimental results, using CASIA and NTERFACE databases, demonstrate that ML outperforms KNN and ANN with recognition rates 41.54% and 26.94% higher, respectively. By adaptively learning global and local features from complete expression images and face parts, our algorithm offers practical and significant advancements in expression recognition.
Published Version
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