Machine Analysis of Facial Expressions

No abstract

Spontaneous vs. posed facial behavior: automatic analysis of brow actions

Past research on automatic facial expression analysis has focused mostly on the recognition of prototypic expressions of discrete emotions rather than on the analysis of dynamic changes over time, although the importance of temporal dynamics of facial expressions for interpretation of the observed facial behavior has been acknowledged for over 20 years. For instance, it has been shown that the temporal dynamics of spontaneous and volitional smiles are fundamentally different from each other. In this work, we argue that the same holds for the temporal dynamics of brow actions and show that velocity, duration, and order of occurrence of brow actions are highly relevant parameters for distinguishing posed from spontaneous brow actions. The proposed system for discrimination between volitional and spontaneous brow actions is based on automatic detection of Action Units (AUs) and their temporal segments (onset, apex, offset) produced by movements of the eyebrows. For each temporal segment of an activated AU, we compute a number of mid-level feature parameters including the maximal intensity, duration, and order of occurrence. We use Gentle Boost to select the most important of these parameters. The selected parameters are used further to train Relevance Vector Machines to determine per temporal segment of an activated AU whether the action was displayed spontaneously or volitionally. Finally, a probabilistic decision function determines the class (spontaneous or posed) for the entire brow action. When tested on 189 samples taken from three different sets of spontaneous and volitional facial data, we attain a 90.7 % correct recognition rate. Categories and Subject Descriptors I.2.10 [Vision and Scene Understanding]: motion, modeling and recovery of physical attribute

Optimising Emotions, Incubating Falsehoods

This open access book deconstructs the core features of online misinformation and disinformation. It finds that the optimisation of emotions for commercial and political gain is a primary cause of false information online. The chapters distil societal harms, evaluate solutions, and consider what must be done to strengthen societies as new biometric forms of emotion profiling emerge. Based on a rich, empirical, and interdisciplinary literature that examines multiple countries, the book will be of interest to scholars and students of Communications, Journalism, Politics, Sociology, Science and Technology Studies, and Information Science, as well as global and local policymakers and ordinary citizens interested in how to prevent the spread of false information worldwide, both now and in the future

An Uneasy Terrain: An Immersive and Speculative Research-Creation

This thesis contemplates the “politicization of vision” by exploring contemporary visualizing technologies that use body and facial recognition to map data in physical and virtual spaces. Through a technological review, this thesis analyzes the emergence of the “social media filter” and examines how this technology not only allows users to morph, alter and extend their digital bodies, but also creates data. Through the literature review I argue that this data contributes to “knowledge creation” for artificial intelligence systems, hence politicizing technologies of vision. Informed by my role as an “active subject” living in a surveilled urban environment, I pay attention to emotions as a guide throughout my creative process. Methodologically, this research-creation renders an immersive and speculative installation engaging bodies in physical space, whereby the audience-participant is materially and virtually present in the projected and captured data. This research-creation contains two pieces that work in tandem; the written document and the installation together make up “An Uneasy Terrain”