Automatic Facial Expression Recognition System Using Convolutional Neural Networks

Ngoc, Hung; Trang, Kien; Vuong, Bao Quoc; Tran, Van-Su; Mai, Linh; Vo, Minh-Thanh; Nguyen, Manh Duc

doi:10.1007/978-981-13-5859-3_82

Cited by 1 publication

(1 citation statement)

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“…The proposed model's performance is assessed by comparing recognition rates with existing studies utilizing the same database. [21] Facial emotion recognition has gained prominence in various applications like social robots, neuromarketing, and games. Non-verbal communication methods, including facial expressions, eye movement, and gestures, play a vital role in human-computer interaction.…”

Section: Related Workmentioning

confidence: 99%

Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization

P V V S Srinivas

2023

IJRITCC

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In facial emotion detection, researchers are actively exploring effective methods to identify and understand facial expressions. This study introduces a novel mechanism for emotion identification using diverse facial photos captured under varying lighting conditions. A meticulously pre-processed dataset ensures data consistency and quality. Leveraging deep learning architectures, the study utilizes feature extraction techniques to capture subtle emotive cues and build an emotion classification model using convolutional neural networks (CNNs). The proposed methodology achieves an impressive 97% accuracy on the validation set, outperforming previous methods in terms of accuracy and robustness. Challenges such as lighting variations, head posture, and occlusions are acknowledged, and multimodal approaches incorporating additional modalities like auditory or physiological data are suggested for further improvement. The outcomes of this research have wide-ranging implications for affective computing, human-computer interaction, and mental health diagnosis, advancing the field of facial emotion identification and paving the way for sophisticated technology capable of understanding and responding to human emotions across diverse domains.

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Section: Related Workmentioning

confidence: 99%

Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization

P V V S Srinivas

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

Automatic Facial Expression Recognition System Using Convolutional Neural Networks

Cited by 1 publication

References 4 publications

Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization

Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization

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