@article{uninipr5423,
            year = {2022},
           pages = {11797},
          number = {22},
           month = {Noviembre},
          author = {Awais Salman Qazi and Muhammad Shoaib Farooq and Furqan Rustam and M{\'o}nica Gracia Villar and Carmen Lil{\'i} Rodr{\'i}guez Velasco and Imran Ashraf},
         journal = {Applied Sciences},
           title = {Emotion Detection Using Facial Expression Involving Occlusions and Tilt},
          volume = {12},
             url = {http://repositorio.unib.org/id/eprint/5423/},
        abstract = {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\%.},
        keywords = {facial expression recognition; convolutional neural network; machine learning; support vector machines}
}