Human-computer interaction (HCI) is expanding towards natural modalities of human expression. Gestures, body movements and other affective interaction techniques can change the way computers interact with humans. In this paper, we propose to extend existing interaction paradigms by including facial expression as a controller in videogames. NovaEmötions is a multiplayer game where players score by acting an emotion through a facial expression. We designed an algorithm to offer an engaging interaction experience using the facial expression. Despite the novelty of the interaction method, our game scoring algorithm kept players engaged and competitive. A user study done with 46 users showed the success and potential for the usage of affective-based interaction in videogames, i.e., the facial expression as the sole controller in videogames. Moreover, we released a novel facial expression dataset with over 41,000 images. These face images were captured in a novel and realistic setting: users playing games where a player's facial expression has an impact on the game score.
Rank fusion is the task of combining multiple ranked document lists (ranks) into a single ranked list. It is a late fusion approach designed to improve the rankings produced by individual systems. Rank fusion techniques have been applied throughout multiple domains: e.g. combining results from multiple retrieval functions, or multimodal search where several feature spaces are common. In this paper, we present the Inverse Square Rank fusion method family, a set of novel fully unsupervised rank fusion methods based on quadratic decay and on logarithmic document frequency normalization. Our experiments created with standard Information Retrieval datasets (image and text fusion) and image datasets (image features fusion), show that ISR outperforms existing rank fusion algorithms. Thus, the proposed technique has comparable or better performance than existing state-of-the-art approaches, while maintaining a low computational complexity and avoiding the need for document scores or training data.
In this paper we propose a high-dimensional indexing technique, based on sparse approximation techniques to speed up the search and retrieval of similar images given a query image feature vector. Feature vectors are stored on an inverted indexed based on a sparsifying dictionary for l0 regression, optimized to reduce the data dimensionality. It concentrates the energy of the original vector on a few coefficients of a higher dimensional representation. The index explores the coefficient locality of the sparse representations, to guide the search through the inverted index. Evaluation on three large-scale datasets showed that our method compares favorably to the state-of-the-art. On a 1 million dataset of SIFT vectors, our method achieved 60.8% precision at 50 by inspecting only 5% of the full dataset, and by using only 1/4 of the time a linear search takes.
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