Scene perception using pareidolia of faces and expressions of emotion

The aim of this study is to simulate the pareidolia capability of humans to produce an emotional response to a scene using analysis of facial expressions associated with abstract face-like patterns. We developed a system that uses a holistic face detector and a facial expression classifier. The υ and SVDD One-Class Support Vector Machines (SVM) were evaluated for creating a holistic face detector, which looks for faces that can vary from natural faces to minimal face-like patterns. A Pairwise Adaptive C and υ-SVM (pa-SVM) were evaluated for creating the facial expression classifier. In both scenarios, a dataset of human faces and facial expressions was used to produce a number of preprocessed images (grayscale, histogram equalised grayscale; and their respective Sobel and Canny edges) at a number of resolutions for analysis. A Gaussian and a degree two polynomial kernel were used with the SVM methods and the results were obtained using a 10 fold cross validation technique. A concern with the face detectors is verifying that they can look for minimal face-like patterns empirically. To address this concern, we created cartoon faces of the human face dataset and degraded these cartoon faces to produce an array of minimal face-like patterns. We then evaluated the face detectors and facial expression classifiers with the best model parameters on these cartoon faces. The outcome is a holistic system with the potential to describe a scene by producing an array of emotion scores corresponding to Ekman's seven Universal Facial Expressions of Emotion.