stdClass Object
(
    [id] => 11017
    [paper_index] => 202307-02-013862
    [title] => FACIAL EMOTIONAL RECOGNITION USING MOBILENET BASED TRANSFER LEARNING
    [description] => 
    [author] => Jenisha A, Aleesha Livingston L
    [googlescholar] => https://scholar.google.co.in/scholar?cluster=15627106325245785014&hl=en&as_sdt=0,5
    [doi] => 
    [year] => 2023
    [month] => July
    [volume] => 8
    [issue] => 7
    [file] => 102pm_39.EPRA JOURNALS 13862.pdf
    [abstract] => In the real world detecting a facial emotion is challenging and complicated. To identify the subtle differences in facial expressions, Facial Emotion Recognition (FER) requires the model to learn. For image recognition tasks, a convolutional neural network (CNN) is a type of deep learning model that is commonly used. CNNs are able to learn features from images that are relevant to the task at hand, such as facial expressions. A pre-trained CNN is a CNN that has already been trained on a large dataset of images for another task, such as image classification. Pre-trained CNNs can be used to improve the performance of CNNs for other tasks, such as facial emotion recognition. The main difference between a CNN and a pre-trainedCNN is that a pre-trained CNN has already learned to extract features from images that are relevant to the task at hand. This means that a pre-trained CNN can be used to improve the performance of a CNN for the task at hand without having to train the CNN from scratch. Here we use MOBILENET as the pre-trained convolution neural network used with the help of the transfer learning technique. MOBILENET is a pre-trained CNN for FER, because it is efficient and accurate.EmoNet is a proposed mobile facial expression recognition system that utilizes the power of transfer learning and the efficiency of the MOBILENET model. The system aims to accurately classify facial expressions in real-time on mobile devices, making it accessible and user-friendly. The data is collected, pre-processed, and fed into the MOBILENET model for feature extraction. Stochastic gradient descent (SGD) is employed to train the pre-processed model, and its performance is evaluated using precision, recall, F1-measure, and accuracy metrics. Through experimental analysis and performance visualization, EmoNet demonstrates high estimation values and superior severity-level classification results compared to other models. This system offers a promising solution for efficient and accurate facial expression recognition, with potential applications in various domains, including emotion detection, human-computer interaction, and social robotics.
    [keywords] => CNN, EmoNet, Facial Emotional Recognition, MOBILENET, Pre-trained CNN.
    [doj] => 2023-07-21
    [hit] => 3233
    [status] => y
    [award_status] => P
    [orderr] => 39
    [journal_id] => 2
    [googlesearch_link] => https://www.google.com/search?q=FACIAL+EMOTIONAL+RECOGNITION+USING+MOBILENET+BASED+TRANSFER+LEARNING+Jenisha+A%2C+Aleesha+Livingston+L&rlz=1C1CHBD_enIN959IN959&sourceid=chrome&ie=UTF-8
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Research & Development (IJRD)
    [short_code] => IJSR
    [eissn] =>  2455-7838 (Online)
    [pissn] => - -
    [home_page_wrapper] => images/products_image/2-n.png
)
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