Machine Analysis of Facial Expressions

No abstract

What does the amygdala contribute to social cognition?

The amygdala has received intense recent attention from neuroscientists investigating its function at the molecular, cellular, systems, cognitive, and clinical level. It clearly contributes to processing emotionally and socially relevant information, yet a unifying description and computational account have been lacking. The difficulty of tying together the various studies stems in part from the sheer diversity of approaches and species studied, in part from the amygdala's inherent heterogeneity in terms of its component nuclei, and in part because different investigators have simply been interested in different topics. Yet, a synthesis now seems close at hand in combining new results from social neuroscience with data from neuroeconomics and reward learning. The amygdala processes a psychological stimulus dimension related to saliency or relevance; mechanisms have been identified to link it to processing unpredictability; and insights from reward learning have situated it within a network of structures that include the prefrontal cortex and the ventral striatum in processing the current value of stimuli. These aspects help to clarify the amygdala's contributions to recognizing emotion from faces, to social behavior toward conspecifics, and to reward learning and instrumental behavior

Deep Hashing Network for Unsupervised Domain Adaptation

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.Comment: CVPR 201

Exploiting multimedia in creating and analysing multimedia Web archives

The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general

The effect of facial expression and identity information on the processing of own and other race faces

The central aim of the current thesis was to examine how facial expression and racial identity information affect face processing involving different races, and this was addressed by studying several types of face processing tasks including face recognition, emotion perception/recognition, face perception and attention to faces. In particular, the effect of facial expression on the differential processing of own and other race faces (the so-called the own-race bias) was examined from two perspectives, examining the effect both at the level of perceptual expertise favouring the processing of own-race faces and in-group bias influencing face processing in terms of a self-enhancing dimension. Results from the face recognition study indicated a possible similarity between familiar/unfamiliar and own-race/other-race face processing. Studies on facial expression perception and memory showed that there was no indication of in-group bias in face perception and memory, although a common finding throughout was that different race faces were often associated with different types of facial expressions. The most consistent finding across all studies was that the effect of the own-race bias was more evident amongst European participants. Finally, results from the face attention study showed that there were no signs of preferential visual attention to own-race faces. The results from the current research provided further evidence to the growing body of knowledge regarding the effects of the own-race bias. Based on this knowledge, for future studies it is suggested that a better understanding of the mechanisms underlying the own-race bias would help advance this interesting and ever-evolving area of research further.University of Stirling PhD studentshi