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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 614))

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Facial Emotion recognition (FER) is a significant research domain in computer vision. FER is considered a challenging task due to emotion-related differences such as heterogeneity of human faces, differences in images due to lighting conditions, angled faces, head poses, different background settings, etc. Moreover, there is also a need for a generalized and efficient model for emotion identification. So, this paper presents a novel, efficient, and generalized DarkSiL (DS) detector for FER that is robust to variation in illumination conditions, face orientation, gender, different ethnicities, and varied background settings. We have introduced a low-cost, smooth, bounded below, and unbounded above Sigmoid-weighted linear unit function in our model to improve efficiency as well as accuracy. The performance of the proposed model is evaluated on four diverse datasets including CK + , FER-2013, JAFFE, and KDEF datasets and achieved an accuracy of 99.6%, 64.9%, 92.9%, and 91%, respectively. We also performed a cross-dataset evaluation to show the generalizability of our DS detector. Experimental results prove the effectiveness of the proposed framework for the reliable identification of seven different classes of emotions.

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This work was supported by the Multimedia Signal Processing Research Lab at the University of Engineering and Technology, Taxila, Pakistan.

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Correspondence to Ali Javed .

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Dar, T., Javed, A. (2023). DarkSiL Detector for Facial Emotion Recognition. In: Anwar, S., Ullah, A., Rocha, Á., Sousa, M.J. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. Springer, Singapore.

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