Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production ["eprint_fieldopt_access_open" not defined] ["eprint_fieldopt_language_en" not defined] Facial emotion recognition (FER) is an important and developing topic of research in the field of pattern recognition. The effective application of facial emotion analysis is gaining popularity in surveillance footage, expression analysis, activity recognition, home automation, computer games, stress treatment, patient observation, depression, psychoanalysis, and robotics. Robot interfaces, emotion-aware smart agent systems, and efficient human–computer interaction all benefit greatly from facial expression recognition. This has garnered attention as a key prospect in recent years. However, due to shortcomings in the presence of occlusions, fluctuations in lighting, and changes in physical appearance, research on emotion recognition has to be improved. This paper proposes a new architecture design of a convolutional neural network (CNN) for the FER system and contains five convolution layers, one fully connected layer with rectified linear unit activation function, and a SoftMax layer. Additionally, the feature map enhancement is applied to accomplish a higher detection rate and higher precision. Lastly, an application is developed that mitigates the effects of the aforementioned problems and can identify the basic expressions of human emotions, such as joy, grief, surprise, fear, contempt, anger, etc. Results indicate that the proposed CNN achieves 92.66% accuracy with mixed datasets, while the accuracy for the cross dataset is 94.94%. metadata Qazi, Awais Salman and Farooq, Muhammad Shoaib and Rustam, Furqan and Gracia Villar, Mónica and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) Emotion Detection Using Facial Expression Involving Occlusions and Tilt. Applied Sciences, 12 (22). p. 11797. ISSN 2076-3417