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This article presents a computer vision approach that can detect and classify abstract face-like patterns, including subliminal faces within a scene. This can be regarded as a way of simulating the phenomenon of pareidolia, that is, the tendency of humans to ‘see faces’ in random structures such as clouds or rocks. The paper describes the system consisting of a component-based face detector and an...
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,...
In a previous publication we showed a component approach performing better than a holistic approach for classifying 6 discrete facial expressions of emotion (happy, sad, surprised, fearful, disgusted and angry) plus the neutral face. In this paper, we present the impact on the two approaches if a seventh facial expression is included. This seventh expression is known as the contemptuous expression...
Emotions are generated and modulated by many factors in the ever-changing surrounding environment. A new and challenging task is to emulate emotional responses on a robot that are caused by visual stimuli, such that the robot's responses mirror that of the human user. This paper presents the initial stage of an affective system that has been trained on-line using reinforcement learning to generate...
This paper describes and experimentally evaluates a new variation of multiclass classification using support vector machines. The technique, called pairwise adaptive support vector machines (pa-SVM), is a one-vs-one multiclass classifier with each binary classifier optimized towards using the best (C,γ) parameter pair to obtain the best correct classification rate. An exponential grid search and a...
Current approaches to facial expression classification employ a variety of expression classes and different preprocessing steps, making comparison of results difficult. To outline the effects of these variations we explore several image and action preprocessing steps, using the discrete expressions: happy, sad, surprised, fearful, angry, disgusted and neutral; with a dataset aligned and normalised...
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