A Study of Stressed Facial Recognition Based on Histogram Information

Abstract

Stress represents our subconscious emotions. The majority of the unconscious content is unacceptable or unpleasant such as pain, anxiety, or conflict. Most individuals do not realize that they are experiencing stress. Prolonged stressful experiences are likely to lead to health problems and affect one's facial appearance, specifically wrinkles shown in the face. This paper discussed the introduction of facial stress with histogram information. There are three stages in recognizing the stress pattern on the face of the registered image, feature extraction and classification. The registered image process takes three important parts of the face, i.e. the eyes, nose, and mouth. The feature extraction process was performed using the histogram method, i.e. Gabor filter and HOG feature. Each extracted feature was used as the model input to determine whether or not an individual is suffering from stress. Two classification methods were applied to learn stress patterns from the extracted feature. The classification process was performed using SVM with six kernel functions and a Tree algorithm with three numbers of split. Each model is trained using ten cross-fold validation strategies. The test results showed that the Gabor filter and HOG feature accuracy was 55% and 65%, respectively.