Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm

2019 ◽  
Vol 78 (18) ◽  
pp. 25753-25779 ◽  
Author(s):  
Manosij Ghosh ◽  
Tuhin Kundu ◽  
Dipayan Ghosh ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Memetic Algorithm ◽  
Facial Emotion Recognition ◽  
Hill Climbing ◽  
Facial Emotion ◽  
Selection For

scholarly journals Deep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM

2021 ◽  
Author(s):  
Kenan DONUK ◽  
Ali ARI ◽  
Mehmet Fatih ÖZDEMİR ◽  
Davut HANBAY
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Deep Feature ◽  
Selection For

scholarly journals Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

2020 ◽  
Vol 10 (8) ◽  
pp. 2816 ◽  
Author(s):  
Soumyajit Saha ◽  
Manosij Ghosh ◽  
Soulib Ghosh ◽  
Shibaprasad Sen ◽  
Pawan Kumar Singh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Local Binary Pattern ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Cosine Similarity ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.

Grey Wolf optimisation‐based feature selection and classification for facial emotion recognition

2018 ◽  
Vol 7 (5) ◽  
pp. 490-499 ◽  
Author(s):  
Ninu Preetha Nirmala Sreedharan ◽  
Brammya Ganesan ◽  
Ramya Raveendran ◽  
Praveena Sarala ◽  
Binu Dennis ◽  
...  
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Grey Wolf

scholarly journals A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

2017 ◽  
Vol 47 (6) ◽  
pp. 1496-1509 ◽  
Author(s):  
Kamlesh Mistry ◽  
Li Zhang ◽  
Siew Chin Neoh ◽  
Chee Peng Lim ◽  
Ben Fielding
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals Self-Learning Facial Emotional Feature Selection Based on Rough Set Theory

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
Yong Yang ◽  
Guoyin Wang ◽  
Hao Kong
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Recognition Rate ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Support Vector ◽  
Self Learning

Emotion recognition is very important for human-computer intelligent interaction. It is generally performed on facial or audio information by artificial neural network, fuzzy set, support vector machine, hidden Markov model, and so forth. Although some progress has already been made in emotion recognition, several unsolved issues still exist. For example, it is still an open problem which features are the most important for emotion recognition. It is a subject that was seldom studied in computer science. However, related research works have been conducted in cognitive psychology. In this paper, feature selection for facial emotion recognition is studied based on rough set theory. A self-learning attribute reduction algorithm is proposed based on rough set and domain oriented data-driven data mining theory. Experimental results show that important and useful features for emotion recognition can be identified by the proposed method with a high recognition rate. It is found that the features concerning mouth are the most important ones in geometrical features for facial emotion recognition.

Beeinträchtigtes Erkennen emotionaler Gesichtsausdrücke durch psychiatrische Patientinnen bei Alexithymie

2013 ◽  
Vol 61 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Daniel Dittrich ◽  
Gregor Domes ◽  
Susi Loebel ◽  
Christoph Berger ◽  
Carsten Spitzer ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Emotional Expression ◽  
Depression Scale ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Check List ◽  
Psychosomatische Erkrankungen ◽  
Psychiatrische Patienten ◽  
Symptom Check List ◽  
Tas 20

Die vorliegende Studie untersucht die Hypothese eines mit Alexithymie assoziierten Defizits beim Erkennen emotionaler Gesichtsaudrücke an einer klinischen Population. Darüber hinaus werden Hypothesen zur Bedeutung spezifischer Emotionsqualitäten sowie zu Gender-Unterschieden getestet. 68 ambulante und stationäre psychiatrische Patienten (44 Frauen und 24 Männer) wurden mit der Toronto-Alexithymie-Skala (TAS-20), der Montgomery-Åsberg Depression Scale (MADRS), der Symptom-Check-List (SCL-90-R) und der Emotional Expression Multimorph Task (EEMT) untersucht. Als Stimuli des Gesichtererkennungsparadigmas dienten Gesichtsausdrücke von Basisemotionen nach Ekman und Friesen, die zu Sequenzen mit sich graduell steigernder Ausdrucksstärke angeordnet waren. Mittels multipler Regressionsanalyse untersuchten wir die Assoziation von TAS-20 Punktzahl und facial emotion recognition (FER). Während sich für die Gesamtstichprobe und den männlichen Stichprobenteil kein signifikanter Zusammenhang zwischen TAS-20-Punktzahl und FER zeigte, sahen wir im weiblichen Stichprobenteil durch die TAS-20 Punktzahl eine signifikante Prädiktion der Gesamtfehlerzahl (β = .38, t = 2.055, p < 0.05) und den Fehlern im Erkennen der Emotionen Wut und Ekel (Wut: β = .40, t = 2.240, p < 0.05, Ekel: β = .41, t = 2.214, p < 0.05). Für wütende Gesichter betrug die Varianzaufklärung durch die TAS-20-Punktzahl 13.3 %, für angeekelte Gesichter 19.7 %. Kein Zusammenhang bestand zwischen der Zeit, nach der die Probanden die emotionalen Sequenzen stoppten, um ihre Bewertung abzugeben (Antwortlatenz) und Alexithymie. Die Ergebnisse der Arbeit unterstützen das Vorliegen eines mit Alexithymie assoziierten Defizits im Erkennen emotionaler Gesichtsausdrücke bei weiblchen Probanden in einer heterogenen, klinischen Stichprobe. Dieses Defizit könnte die Schwierigkeiten Hochalexithymer im Bereich sozialer Interaktionen zumindest teilweise begründen und so eine Prädisposition für psychische sowie psychosomatische Erkrankungen erklären.

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