**Author details**

Fatima Isiaka1 , Kassim Mwitondi<sup>1</sup> and Adamu M. Ibrahim2 \*

\*Address all correspondence to: scami@leeds.ac.uk

1 Department of Computing, Sheffield Hallam University, UK

2 University of Leeds, UK

## **References**


[4] Demiral SB, Schlesewsky M, Bornkessel-Schlesewsky I. On the universality of language comprehension strategies: Evidence from turkish. Cognition. 2008;**106**(1):484-500

possibility of a good performance on the system model. The response output illustrates the

This chapter has offered an evaluative perspective on an important aspect in user interphase on game, web, and text. By introducing a novel approach to user interaction and user physiological response, we integrated eye movement and physiological response to determine correlates that serve as tertiary indicators of the stress levels of a user based on attributes obtained from their physiological response. The benefits of the proposed model have proved to be reliable, given the results and findings from the response output of the system and the model has offered some solutions to the persistent user interaction and physiological association, which may be sustainable in the long-term with further evaluations and validations. The method used here provides an automated way of assessing human stress levels when dealing with specific visual contents. This is an important achievement and in that it is able to predict what contents on a visual interphase course stress-induced emotion in users during interaction. This could be applied to other areas like Internet security, triggering alarm for unauthorized access, or abnormal activities online which is the basis for our future work and also in testing performance. This chapter opens the way to possible benefits in terms of predicting human behavior in respect to Internet security by using the process as an alarm trigger for sending alerts on unauthorized access or abnormal activities online; this can be done by detecting user

and Adamu M. Ibrahim2

[1] Andreassi JL. Psychophysiology: Human Behavior and Physiological Response. Psy-

[2] Cooley R, Mobasher B, Srivastava J. Data preparation for mining world wide web brows-

[3] De Santos A, Sanchez-Avila C, Guerra-Casanova J, Pozo GB-D. Real-time stress detection by means of physiological signals. In: Recent Application in Biometrics. Rijeka: InTech; 2011

ing patterns. Knowledge and Information Systems. 1999;**1**(1):5-32

\*

most significant response magnitude at order 3 (**Figure 8b**).

**6. Conclusion**

36 Human-Robot Interaction - Theory and Application

emotion on the visual interphase.

, Kassim Mwitondi<sup>1</sup>

\*Address all correspondence to: scami@leeds.ac.uk

1 Department of Computing, Sheffield Hallam University, UK

**Author details**

2 University of Leeds, UK

chology Press; 2000

Fatima Isiaka1

**References**


**Chapter 3**

Provisional chapter

**Review on Emotion Recognition Databases**

DOI: 10.5772/intechopen.72748

Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and infor-

With facial recognition and human-computer interaction becoming more prominent with each passing year, the amount of databases associated with both face detection and facial expressions has grown immensely [1, 2]. A key part in creating, training and even evaluating supervised emotion recognition models is a well-labelled database of visual and/or audio information fit for the desired application. For example, emotion recognition has many different applications ranging from simple human-robot computer interaction [3–5] to automated

There are several papers, blogs and books [7–10] fully dedicated to just describing some of the more prominent databases for face recognition. However, the collection of emotion databases is disparate, as they are often tailored to a specific purpose, so there is no complete and

> © The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

Review on Emotion Recognition Databases

Rain Eric Haamer, Eka Rusadze, Iiris Lüsi, Tauseef Ahmed, Sergio Escalera and

Rain Eric Haamer, Eka Rusadze, Iiris Lüsi, Tauseef Ahmed, Sergio Escalera and

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72748

mative tables based on the format.

Keywords: emotion, computer vision, databases

thorough overview of the ones that currently exist.

Gholamreza Anbarjafari

Gholamreza Anbarjafari

Abstract

1. Introduction

depression detection [6].

#### **Chapter 3** Provisional chapter

#### **Review on Emotion Recognition Databases** Review on Emotion Recognition Databases

DOI: 10.5772/intechopen.72748

Rain Eric Haamer, Eka Rusadze, Iiris Lüsi, Tauseef Ahmed, Sergio Escalera and Gholamreza Anbarjafari Rain Eric Haamer, Eka Rusadze, Iiris Lüsi, Tauseef Ahmed, Sergio Escalera and Gholamreza Anbarjafari

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72748

#### Abstract

Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format.

Keywords: emotion, computer vision, databases
