CNN Deep Learning for Facial Expression Recognition

CNN Deep Learning for Facial Expression Recognition applied on CK+ dataset
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Updated 26 May 2024

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In this study, we developed a Convolutional Neural Network (CNN) for facial expression recognition utilizing the CK+ (Cohn-Kanade) dataset, achieving an impressive accuracy of 99.97%. The model architecture was meticulously designed to capture intricate facial features through multiple convolutional and pooling layers, followed by fully connected layers to perform classification. The CK+ dataset, known for its comprehensive collection of annotated facial expressions, served as an ideal benchmark for training and validating our CNN model. Advanced data augmentation techniques and hyperparameter optimization were employed to enhance the model's generalization capabilities. Our results demonstrate the potential of deep learning approaches in achieving near-perfect accuracy in facial expression recognition, highlighting the efficacy of CNNs in processing and interpreting complex visual data.

Cite As

Farid AYECHE (2024). CNN Deep Learning for Facial Expression Recognition (https://www.mathworks.com/matlabcentral/fileexchange/166671-cnn-deep-learning-for-facial-expression-recognition), MATLAB Central File Exchange. Retrieved .

Ayeche, Farid & Adel, Alti. (2021). HDG and HDGG:an extensible feature extraction descriptor for effective face and facial expressions recognition. Pattern Analysis and Applications. 24. 10.1007/s10044-021-00972-2.

Ayeche F, Alti A. Local directional gradients extension for recognising face and facial expressions. Int J Intell Syst Technol Appl. 2022;20(6):487–509. DOI: https://doi.org/10.1504/ijista.2022.128525

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Created with R2020a
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Version Published Release Notes
1.0.0