A facial component-based system for emotion classification

7Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

Abstract

Smart environments with ubiquitous computers are the next generation of information technology, which requires improved human{computer interfaces. That is, the computer of the future must be aware of the people in its environment; it must know their identities and must understand their moods. Despite the great effort made in the past decades, the development of a system capable of automatic facial emotion recognition is still rather difficult. In this paper, we challenge the benchmark algorithm on emotion classification of the Extended Cohn-Kanade (CK+) database, and we present a facial component-based system for emotion classification, which beats the given benchmark performance: using a 2D emotional face, we searched for highly discriminative areas, we classified them independently, and we fused all results together to allow for facial emotion recognition. The use of the sparse-representation-based classifier allows for the automatic selection of the two most successful blocks and obtains the best results by beating the given benchmark performance by six percentage points. Finally, using the most promising algorithms for facial analysis, we created equivalent facial component-based systems and we made a fair comparison among them.

References Powered by Scopus

Compressed sensing

25405Citations
N/AReaders
Get full text

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

13805Citations
N/AReaders
Get full text

An introduction to compressive sampling: A sensing/sampling paradigm that goes against the common knowledge in data acquisition

9028Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech

48Citations
N/AReaders
Get full text

Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine

15Citations
N/AReaders
Get full text

Optimal threshold determination for discriminating driving anger intensity based on EEG wavelet features and ROC curve analysis

13Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Sönmez, E., & Albayrak, S. (2016). A facial component-based system for emotion classification. Turkish Journal of Electrical Engineering and Computer Sciences, 24(3), 1663–1673. https://doi.org/10.3906/elk-1401-18

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 6

67%

Professor / Associate Prof. 1

11%

Lecturer / Post doc 1

11%

Researcher 1

11%

Readers' Discipline

Tooltip

Computer Science 7

64%

Engineering 2

18%

Business, Management and Accounting 1

9%

Psychology 1

9%

Save time finding and organizing research with Mendeley

Sign up for free