Albert C. Cruz

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Communication between humans is rich in complexity and is not limited to verbal signals; emotions are conveyed with gesture, pose and facial expression. Facial Emotion Recognition and Analysis (FERA), the set of techniques by which non-verbal communication is quantified, is an exemplar case where humans consistently outperform computer methods. While the(More)
Affective computing-the emergent field in which computers detect emotions and project appropriate expressions of their own-has reached a bottleneck where algorithms are not able to infer a person's emotions from natural and spontaneous facial expressions captured in video. While the field of emotion recognition has seen many advances in the past decade, a(More)
This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image(More)
Facial emotion recognition in unconstrained settings is a difficult task. They key problems are that people express their emotions in ways that are different from other people, and, for large datasets, there are not enough examples of a specific person to model his/her emotion. A model for predicting emotions will not generalize well to predicting the(More)
In the field of facial emotion recognition, early research advancedwith the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization(More)
The Emotion Recognition in the Wild challenge poses significant problems to state of the art auditory and visual affect quantification systems. To overcome the challenges, we investigate supplementary meta features based on film semiotics. Movie scenes are often presented and arranged in such a way as to amplify the emotion interpreted by the viewing(More)
Facial emotion recognition from video is an exemplar case where both humans and computers underperform. In recent emotion recognition competitions, top approaches were using either geometric relationships that best captured facial dynamics or an accurate registration technique to develop appearance features. These two methods capture two different types of(More)