Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. The findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. The proposed strategy is based on the generation of artificial ‘emotional’ images on the face using Deep Neural Self Attention GANs (DNSAGANs). This way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. This larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FER2013 dataset is used. The training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. The percentage of accuracy is about $88.64 \%$ for 100 epochs using the proposed optimizer techniques.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Artificial Intelligence based Facial Emotion Recognition with Deep Neural GAN Augmentation


    Contributors:


    Publication date :

    2024-11-06


    Size :

    392143 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Artificial intelligence facial recognition device

    SU JING / LIANG JIANMIN / ZHU JIAYI et al. | European Patent Office | 2024

    Free access

    Automatic Human Emotion Recognition System using Facial Expressions with Convolution Neural Network

    MADUPU, RAM KUMAR / KOTHAPALLI, CHIRANJEEVI / YARRA, VASANTHI et al. | IEEE | 2020


    An Emotion Model Based on Artificial Intelligence

    Fang, Y. / Chen, Z.-q. / Yuan, Z.-z. | British Library Online Contents | 2006


    Pose-invariant descriptor for facial emotion recognition

    Shojaeilangari, S. / Yau, W. Y. / Teoh, E. K. | British Library Online Contents | 2016


    Facial emotion recognition and adaptative postural reaction by a humanoid based on neural evolution

    García Bueno, Jorge / González-Fierro, Miguel / Moreno, Luis et al. | BASE | 2013

    Free access