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@article{168645,
        author = {MADHUMATHI S and GOBINATH S},
        title = {Facial Emotion Detection Using Convolutional Neural Networks Bridging the Gap Between AI and Human Interaction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1490-1495},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168645},
        abstract = {A key requirement for developing any innovative system in a computing environment is to integrate a sufficiently friendly interface with the average end user. Accurate design of such a user-centered interface, however, means more than just the ergonomics of the panels and displays. It also requires designers to specify what, how, where, and when to use information. Facial expression as a natural, non-intrusive, and efficient way of communicating has been considered as one of the potential inputs of such interfaces.
The work of this thesis aims at designing a robust Facial Expression Recognition (FER) system by combining various techniques from computer vision and pattern recognition. Expression recognition is closely related to face recognition, where a lot of research has been done and a vast array of algorithms has been introduced. FER can also be considered a special case of a pattern recognition problem, and many techniques are available. In the designing of an FER system, we can take advantage of these resources and use existing algorithms as building blocks of our system. So a major part of this work is to determine the optimal combination of algorithms. To do this, we first divide the system into 3 modules.
Namely feature extraction, classification, and preprocessing. After that, several candidate techniques are applied for each of them, and finally, by evaluating the effectiveness of various configurations, the ideal setup is identified. 
The classifier, which is the heart of the facial expression recognition system, is another topic that fascinates designers of such systems greatly. The assumption made by traditional classification algorithms is that the image is a single variable function of the underlying class label. This is untrue in the face recognition domain, though, where a variety of characteristics, such as identity, expression, illumination, and so forth, affect how a face appears.},
        keywords = {FER, CNN, recognition, emotions},
        month = {October},
        }