3,356 research outputs found
Developing a comprehensive framework for multimodal feature extraction
Feature extraction is a critical component of many applied data science
workflows. In recent years, rapid advances in artificial intelligence and
machine learning have led to an explosion of feature extraction tools and
services that allow data scientists to cheaply and effectively annotate their
data along a vast array of dimensions---ranging from detecting faces in images
to analyzing the sentiment expressed in coherent text. Unfortunately, the
proliferation of powerful feature extraction services has been mirrored by a
corresponding expansion in the number of distinct interfaces to feature
extraction services. In a world where nearly every new service has its own API,
documentation, and/or client library, data scientists who need to combine
diverse features obtained from multiple sources are often forced to write and
maintain ever more elaborate feature extraction pipelines. To address this
challenge, we introduce a new open-source framework for comprehensive
multimodal feature extraction. Pliers is an open-source Python package that
supports standardized annotation of diverse data types (video, images, audio,
and text), and is expressly with both ease-of-use and extensibility in mind.
Users can apply a wide range of pre-existing feature extraction tools to their
data in just a few lines of Python code, and can also easily add their own
custom extractors by writing modular classes. A graph-based API enables rapid
development of complex feature extraction pipelines that output results in a
single, standardized format. We describe the package's architecture, detail its
major advantages over previous feature extraction toolboxes, and use a sample
application to a large functional MRI dataset to illustrate how pliers can
significantly reduce the time and effort required to construct sophisticated
feature extraction workflows while increasing code clarity and maintainability
Automatic Measurement of Affect in Dimensional and Continuous Spaces: Why, What, and How?
This paper aims to give a brief overview of the current state-of-the-art in automatic measurement of affect signals in dimensional and continuous spaces (a continuous scale from -1 to +1) by seeking answers to the following questions: i) why has the field shifted towards dimensional and continuous interpretations of affective displays recorded in real-world settings? ii) what are the affect dimensions used, and the affect signals measured? and iii) how has the current automatic measurement technology been developed, and how can we advance the field
The knowledge domain of affective computing: a scientometric review
Purpose – The aim of this study is to investigate the bibliographical information about Affective Computing identifying advances, trends, major papers, connections, and areas of research. Design/methodology/approach – A scientometric analysis was applied using CiteSpace, of 5,078 references about Affective Computing imported from the Web-of-Science Core Collection, covering the period of 1991-2016. Findings – The most cited, creative, burts and central references are displayed by areas of research, using metrics and througout-time visualization. Research limitations/implications – Interpretation is limited to references retrieved from theWeb-of-Science Core Collection in the fields of management, psychology and marketing. Nevertheless, the richness of bibliographical data obtained, largely compensates this limitation. Practical implications – The study provides managers with a sound body of knowledge on Affective Computing, with which they can capture general public emotion in respect of their products and services, and on which they can base their marketing intelligence gathering, and strategic planning. Originality/value – The paper provides new opportunities for companies to enhance their capabilities in terms of customer relationships.info:eu-repo/semantics/acceptedVersio
Recognising Complex Mental States from Naturalistic Human-Computer Interactions
New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer
Recognising Complex Mental States from Naturalistic Human-Computer Interactions
New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer
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