Garuda - Garba Rujukan Digital

Rania S. El-Sayed

Al-Azhar University

Author-ID : 2466949

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document

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Articles

Local Entropy and Standard Deviation for Facial Expressions Recognition in Thermal Imaging Yomna M. Elbarawy; Rania S. El-Sayed; Neveen I. Ghali
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Emotional reactions are the best way to express human attitude and thermal imaging mainly used to utilize detection of temperature variations as in detecting spatial and temporal variation in the water status of grapevine. By merging the two facts this paper presents the Discrete Cosine Transform (DCT) with Local Entropy (LE) and Local Standard Deviation (LSD) features as an efficient filters for investigating human emotional state in thermal images. Two well known classifiers, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were combined with the earlier features and applied over a database with variant illumination, as well as occlusion by glasses and poses to generate a recognition model of facial expressions in thermal images. KNN based on DCT and LE gives the best accuracy compared with other classifier and features results.

Local Entropy and Standard Deviation for Facial Expressions Recognition in Thermal Imaging Yomna M. Elbarawy; Rania S. El-Sayed; Neveen I. Ghali
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v7i4.1230

Emotional reactions are the best way to express human attitude and thermal imaging mainly used to utilize detection of temperature variations as in detecting spatial and temporal variation in the water status of grapevine. By merging the two facts this paper presents the Discrete Cosine Transform (DCT) with Local Entropy (LE) and Local Standard Deviation (LSD) features as an efficient filters for investigating human emotional state in thermal images. Two well known classifiers, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were combined with the earlier features and applied over a database with variant illumination, as well as occlusion by glasses and poses to generate a recognition model of facial expressions in thermal images. KNN based on DCT and LE gives the best accuracy compared with other classifier and features results.

Local Entropy and Standard Deviation for Facial Expressions Recognition in Thermal Imaging Yomna M. Elbarawy; Rania S. El-Sayed; Neveen I. Ghali
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Emotional reactions are the best way to express human attitude and thermal imaging mainly used to utilize detection of temperature variations as in detecting spatial and temporal variation in the water status of grapevine. By merging the two facts this paper presents the Discrete Cosine Transform (DCT) with Local Entropy (LE) and Local Standard Deviation (LSD) features as an efficient filters for investigating human emotional state in thermal images. Two well known classifiers, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were combined with the earlier features and applied over a database with variant illumination, as well as occlusion by glasses and poses to generate a recognition model of facial expressions in thermal images. KNN based on DCT and LE gives the best accuracy compared with other classifier and features results.

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