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Author: search.filters.author.Annappa, B.Subject: search.filters.subject.Face recognition

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    A comprehensive review of facial expression recognition techniques
    (Springer Science and Business Media Deutschland GmbH, 2023) Rashmi Adyapady, R.R.; Annappa, B.
    Emotion recognition has opened up many challenges, which lead to various advances in computer vision and artificial intelligence. The rapid development in this field has encouraged the development of an automatic system that could accurately analyze and measure the emotions of human beings via facial expressions. This study mainly focuses on facial expression recognition from visual cues, as visual information is the most prominent channel for social communication. The paper provides a comprehensive review of recent advancements in algorithm development, presents the overall findings performed over the past decades, discusses their advantages and constraints. It explores the transition from the laboratory-controlled environment to challenging real-world (in-the-wild) conditions, focusing on essential issues that require further exploration. Finally, relevant opportunities in this field, challenges, and future directions mentioned in this paper assist the researchers and academicians in designing efficient and robust facial expression recognition systems. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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    An ensemble approach using a frequency-based and stacking classifiers for effective facial expression recognition
    (Springer, 2023) Adyapady R, R.; Annappa, B.
    Facial Expression Recognition is an essential aspect of human behavior to communicate effectively. A more profound understanding of human behavior, accurate analysis, and interpretation of the emotional content is essential. Hence, facial features play a crucial role as they contain beneficial information about facial expressions. A baseline architecture belonging to the EfficientNet family of models is explored for feature extraction. In this work, two novel strategies, the ensemble model using the frequency-based voting approach (FV-EffNet) and the stacking classifier (SC-EffNet), are proposed to enhance classification results’ performance. The proposed system deals with both profile and frontal pose variations. The combination of deep learning models with a stacking classifier gave the best results of 98.35% and 98.06%, and the frequency-based approach used with the ensemble classifier achieved superior performance of 98.71% and 98.56% on Oulu-CASIA and RaFD datasets, respectively. The experiment results with the proposed methodology showed better performance than previous studies on Oulu-CASIA and RaFD datasets, making it more robust to pose variations. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.