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Detection of Facial Cues in Digital Images using Computer Vision

The face is one of the most powerful communication tools used to convey physical and emotional state, as well as social behavior through facial cues. In modern technology, computers can interpret these facial cues with the use of computer vision. The objective of this study aims to use the principle of computer vision using the combination of the Haar Cascade Classifiers and Convolutional Neural Network (CNN) to extract information from a digital image and identify if the image contains the facial cues: droopy eyelids, swollen eyelids, eye redness, dark circles, and droopy mouth. In the results of Haar Cascade classifier in detecting the facial features, it yielded an average acceptance rate of 96.78%, and using a multi-label confusion matrix for the CNN model, the result yielded an accuracy of 77% thus proved the detection of the different facial cues using the trained models.