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A Multi-Population FA for Automatic Facial Emotion Recognition. / Mistry, Kamlesh; Rizvi, Baqar ; Rook, Chris; Iqbal, Sadaf ; Zhang, Li; Joy, Colin Paul .

A Multi-Population FA for Automatic Facial Emotion Recognition. International Joint Conference on Neural Networks (IJCNN) : IEEE, 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Published

Standard

A Multi-Population FA for Automatic Facial Emotion Recognition. / Mistry, Kamlesh; Rizvi, Baqar ; Rook, Chris; Iqbal, Sadaf ; Zhang, Li; Joy, Colin Paul .

A Multi-Population FA for Automatic Facial Emotion Recognition. International Joint Conference on Neural Networks (IJCNN) : IEEE, 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvard

Mistry, K, Rizvi, B, Rook, C, Iqbal, S, Zhang, L & Joy, CP 2020, A Multi-Population FA for Automatic Facial Emotion Recognition. in A Multi-Population FA for Automatic Facial Emotion Recognition. IEEE, International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9207516

APA

Mistry, K., Rizvi, B., Rook, C., Iqbal, S., Zhang, L., & Joy, C. P. (2020). A Multi-Population FA for Automatic Facial Emotion Recognition. In A Multi-Population FA for Automatic Facial Emotion Recognition IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207516

Vancouver

Mistry K, Rizvi B, Rook C, Iqbal S, Zhang L, Joy CP. A Multi-Population FA for Automatic Facial Emotion Recognition. In A Multi-Population FA for Automatic Facial Emotion Recognition. International Joint Conference on Neural Networks (IJCNN): IEEE. 2020 https://doi.org/10.1109/IJCNN48605.2020.9207516

Author

Mistry, Kamlesh ; Rizvi, Baqar ; Rook, Chris ; Iqbal, Sadaf ; Zhang, Li ; Joy, Colin Paul . / A Multi-Population FA for Automatic Facial Emotion Recognition. A Multi-Population FA for Automatic Facial Emotion Recognition. International Joint Conference on Neural Networks (IJCNN) : IEEE, 2020.

BibTeX

@inproceedings{eaf1aabef83c42ecb677e015a534edf7,

title = "A Multi-Population FA for Automatic Facial Emotion Recognition",

abstract = "Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models.",

author = "Kamlesh Mistry and Baqar Rizvi and Chris Rook and Sadaf Iqbal and Li Zhang and Joy, {Colin Paul}",

year = "2020",

month = sep,

day = "28",

doi = "10.1109/IJCNN48605.2020.9207516",

language = "English",

isbn = "9781728169262",

booktitle = "A Multi-Population FA for Automatic Facial Emotion Recognition",

publisher = "IEEE",

}

RIS

TY - GEN

T1 - A Multi-Population FA for Automatic Facial Emotion Recognition

AU - Mistry, Kamlesh

AU - Rizvi, Baqar

AU - Rook, Chris

AU - Iqbal, Sadaf

AU - Zhang, Li

AU - Joy, Colin Paul

PY - 2020/9/28

Y1 - 2020/9/28

N2 - Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models.

AB - Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models.

U2 - 10.1109/IJCNN48605.2020.9207516

DO - 10.1109/IJCNN48605.2020.9207516

M3 - Conference contribution

SN - 9781728169262

BT - A Multi-Population FA for Automatic Facial Emotion Recognition

PB - IEEE

CY - International Joint Conference on Neural Networks (IJCNN)

ER -