Affect Recognition in Ads with Application to Computational Advertising

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

Films, Affective Computing and Aesthetic Experience: Identifying Emotional and Aesthetic Highlights from Multimodal Signals in a Social Setting.

Over the last years, affective computing has been strengthening its ties with the humanities, exploring and building understanding of people’s responses to specific artistic multimedia stimuli. “Aesthetic experience” is acknowledged to be the subjective part of some artistic exposure, namely, the inner affective state of a person exposed to some artistic object. In this work, we describe ongoing research activities for studying the aesthetic experience of people when exposed to movie artistic stimuli. To do so, this work is focused on the definition of emotional and aesthetic highlights in movies and studies the people responses to them using physiological and behavioral signals, in a social setting. In order to examine the suitability of multimodal signals for detecting highlights, we initially evaluate a supervised highlight detection system. Further, in order to provide an insight on the reactions of people, in a social setting, during emotional and aesthetic highlights, we study two unsupervised systems. Those systems are able to (a) measure the distance among the captured signals of multiple people using the dynamic time warping algorithm and (b) create a reaction profile for a group of people that would be indicative of whether that group reacts or not at a given time. The results indicate that the proposed systems are suitable for detecting highlights in movies and capturing some form of social interactions across different movie genres. Moreover, similar social interactions during exposure to emotional and some types of aesthetic highlights, such as those corresponding to technical or lightening choices of the director, can be observed. The utilization of electrodermal activity measurements yields in better performances than those achieved when using acceleration measurements, whereas fusion of the modalities does not appear to be beneficial for the majority of the cases

Affect Analysis in Video

Ph.DDOCTOR OF PHILOSOPH

Aesthetic Highlight Detection in Movies Based on Synchronization of Spectators’ Reactions.

Detection of aesthetic highlights is a challenge for understanding the affective processes taking place during movie watching. In this paper we study spectators’ responses to movie aesthetic stimuli in a social context. Moreover, we look for uncovering the emotional component of aesthetic highlights in movies. Our assumption is that synchronized spectators’ physiological and behavioral reactions occur during these highlights because: (i) aesthetic choices of filmmakers are made to elicit specific emotional reactions (e.g. special effects, empathy and compassion toward a character, etc.) and (ii) watching a movie together causes spectators’ affective reactions to be synchronized through emotional contagion. We compare different approaches to estimation of synchronization among multiple spectators’ signals, such as pairwise, group and overall synchronization measures to detect aesthetic highlights in movies. The results show that the unsupervised architecture relying on synchronization measures is able to capture different properties of spectators’ synchronization and detect aesthetic highlights based on both spectators’ electrodermal and acceleration signals. We discover that pairwise synchronization measures perform the most accurately independently of the category of the highlights and movie genres. Moreover, we observe that electrodermal signals have more discriminative power than acceleration signals for highlight detection

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments