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In this paper we propose a novel aesthetic model emphasizing psychovisual statistics extracted from multiple levels in contrast to earlier approaches that rely only on descriptors suited for image recognition or based on photographic principles. At the lowest level, we determine dark-channel, sharpness and eye-sensitivity statistics over rectangular cells within a frame. At the next level, we extract Sentibank features (1, 200 pre-trained visual classifiers) on a given frame, that invoke specific sentiments such as "colorful clouds", "smiling face" etc. and collect the classifier responses as framelevel statistics. At the topmost level, we extract trajectories from video shots. Using viewer's fixation priors, the trajectories are labeled as foreground, and background/camera on which statistics are computed. Additionally, spatio-temporal local binary patterns are computed that capture texture variations in a given shot. Classifiers are trained on individual feature representations independently. On thorough evaluation of 9 different types of features, we select the best features from each level -dark channel, affect and camera motion statistics. Next, corresponding classifier scores are integrated in a sophisticated low-rank fusion framework to improve the final prediction scores. Our approach demonstrates strong correlation with human prediction on 1, 000 broadcast quality videos released by NHK as an aesthetic evaluation dataset.

A person's face discloses important information about their affective state. Although there has been extensive research on recognition of facial expressions, the performance of existing approaches is challenged by facial occlusions. Facial occlusions are often treated as noise and discarded in recognition of affective states. However, hand over face occlusions can provide additional information for recognition of some affective states such as curiosity, frustration and boredom. One of the reasons that this problem has not gained attention is the lack of naturalistic occluded faces that contain hand over face occlusions as well as other types of occlusions. Traditional approaches for obtaining affective data are time demanding and expensive, which limits researchers in affective computing to work on small datasets. This limitation affects the generalizability of models and deprives researchers from taking advantage of recent advances in deep learning that have shown great success in many fields but require large volumes of data. In this paper, we first introduce a novel framework for synthesizing naturalistic facial occlusions from an initial dataset of non-occluded faces and separate images of hands, reducing the costly process of data collection and annotation. We then propose a model for facial occlusion type recognition to differentiate between hand over face occlusions and other types of occlusions such as scarves, hair, glasses and objects. Finally, we present a model to localize hand over face occlusions and identify the occluded regions of the face.

Curiosity plays a crucial role in learning and education of children. Given its complex nature, it is extremely challenging to automatically understand and recognize it. In this paper, we discuss the contexts under which curiosity can be elicited and provide an associated taxonomy. We present an initial empirical study of curiosity that includes the analysis of cooccurring emotions and the valence associated with it, together with gender-specific analysis. We also discuss the visual, acoustic and verbal behavior indicators of curiosity. Our discussions and analysis uncover some of the underlying complexities of curiosity and its temporal evolution, which is a step towards its automatic understanding and recognition. Finally, considering the central role of curiosity in education, we present two education-centered application areas that could greatly benefit from its automatic recognition.