Advancements in Human Eye Micro-Expression Detection Using Convolutional Neural Networks: Database Development and Real-Time Application in Security and Education
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Abstract
A convolutional neural network (CNN) represents a type of artificial neural network distinguished by its efficiency in handling complex artificial intelligence tasks. It presents new opportunities for the pattern recognition of micro-expressions characterized by short duration and small movement amplitude. In the realm of micro-expression recognition research, studies focusing exclusively on eye micro-expressions are limited, yet their significance should not be underestimated. This paper's primary contributions include the creation of a database dedicated to human eye expressions. By segmenting existing facial expression databases and isolating only the eye region, a specialized micro-expression database for the human eye was established. Utilizing TensorFlow, CNN is employed to train, predict, classify, and identify facial expressions within the human eye. This approach successfully achieves micro-expression recognition in the eye area. Additionally, the integration of CUDA significantly reduces the training time of the system model.
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