@inproceedings{scholars19026,
           pages = {204--208},
         journal = {IEEE Region 10 Annual International Conference, Proceedings/TENCON},
           title = {Investigation on Light-Weight Deep Learning Model for Emotion Recognition Using Facial Expressions},
            year = {2023},
             doi = {10.1109/TENCON58879.2023.10322470},
            note = {cited By 0; Conference of 38th IEEE Region 10 Conference, TENCON 2023 ; Conference Date: 31 October 2023 Through 3 November 2023; Conference Code:194660},
        keywords = {Convolutional neural networks; Face recognition; Long short-term memory; Speech recognition, Accurate prediction; ART networks; Clinical relevance- highly accurate prediction from proposed lightweight architecture may aid the accessibility of low computational power device to emotion recognition; Computational power; Emotion recognition; Facial Expressions; Highly accurate; Lightweight architecture; Power devices; State of the art, Emotion Recognition},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179525966&doi=10.1109\%2fTENCON58879.2023.10322470&partnerID=40&md5=09f07c9b943281abc02fe99641091f11},
        abstract = {Research findings have unveiled that facial expressions possess the ability to convey a variety of intense emotions. Hence, in this study, a deep-learning based approach, 2-Dimensional Convolutional Neural Network (2D CNN) for facial emotion recognition is proposed. The proposed network is running at least 47.28 times lesser number of parameters at 542,136, compared to the state-of-the-art (SOTA) network from RAVDESS dataset. The saving from reduced parameters is expected to translate into faster execution in real time. The proposed network scored accuracy of 92 and 94 that outperformed majority of the SOTA networks trained on RAVDESS and SAVEE dataset respectively, except one LSTM network from RAVDESS dataset that scored 98.90 in accuracy but with 116.5x higher number of parameters. {\^A}{\copyright} 2023 IEEE.},
          author = {Ding, S. Y. and Tang, T. B. and Lu, C.-K.}
}