Many applications pointed to the informative potential of the human eyes. In this paper we investigate the possibility of estimating the cognitive process used by a person when addressing a mental challenge, according to the Eye Accessing Cue (EAC) model from the Neuro-Linguistic Programming (NLP) theory [3]. This model states that there is a subtle, yet firm, connection between the non-visual gaze direction and the mental representation system used. From the point of view of computer vision, this work deals with gaze estimation under passive illumination. Using a multistage fusion approach, we show that it is possible to achieve highly accurate results in both terms of eye gaze localization or EAC case recognition.

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To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. Our experimental results suggest that accurate recognition is achievable by a combination of various categories.

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Abstract. This paper presents an automatic system for the recognition of artistic genre in digital representations of paintings. This solution comes as part of the recent extensive effort of developing image processing solutions that facilitate a better understanding of art. As art addresses human perception, the current extracted features are perceptually inspired. While 3D Color Histogram and Gabor Filter Energy have been used for art description, frameworks extracted using anchoring theory are novel in this field. The paper investigates the possible use of 7 classifiers and the resulting performance, as evaluated on a database containing more than 3400 paintings from 6 different genres, outperforms the reported state of the art.