{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:18:03Z","timestamp":1741666683074,"version":"3.38.0"},"reference-count":38,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2021,6,29]]},"abstract":"<jats:p>Face Expression Recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.<\/jats:p>","DOI":"10.3233\/idt-190181","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T16:40:08Z","timestamp":1621960808000},"page":"179-187","source":"Crossref","is-referenced-by-count":3,"title":["Concise CNN model for face expression recognition"],"prefix":"10.1177","volume":"15","author":[{"given":"Harshadkumar B.","family":"Prajapati","sequence":"first","affiliation":[]},{"given":"Ankit S.","family":"Vyas","sequence":"additional","affiliation":[]},{"given":"Vipul K.","family":"Dabhi","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/IDT-190181_ref1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1037\/0022-3514.53.1.53","article-title":"Mood effects on person-perception judgments","volume":"53","author":"Forgas","year":"1987","journal-title":"Journal of Personality and Social Psychology"},{"key":"10.3233\/IDT-190181_ref2","doi-asserted-by":"crossref","first-page":"3119","DOI":"10.1109\/IROS.2006.282331","article-title":"Control of facial expressions of the humanoid robot head Roman","author":"Berns","year":"2006","journal-title":"2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems"},{"key":"10.3233\/IDT-190181_ref3","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/CVPRW.2003.10057","article-title":"Real time face detection and facial expression recognition: Development and applications to human computer interaction","volume":"5","author":"Bartlett","year":"2003","journal-title":"2003 Conference on Computer Vision and Pattern Recognition Workshop"},{"issue":"6","key":"10.3233\/IDT-190181_ref4","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/j.imavis.2008.08.005","article-title":"Facial expression recognition based on local binary patterns: A comprehensive study","volume":"27","author":"Shan","year":"2009","journal-title":"Image and Vision Computing"},{"issue":"1","key":"10.3233\/IDT-190181_ref5","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1162\/jocn.1991.3.1.71","article-title":"Eigenfaces for recognition","volume":"3","author":"Turk","year":"1991","journal-title":"Journal of Cognitive Neuroscience"},{"issue":"2","key":"10.3233\/IDT-190181_ref6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/BF00204594","article-title":"Gabor filters as texture discriminator","volume":"61","author":"Fogel","year":"1989","journal-title":"Biological Cybernetics"},{"key":"10.3233\/IDT-190181_ref7","first-page":"1","article-title":"Survey and analysis of extraction of human face features","author":"Brahmbhatt","year":"2017","journal-title":"2017 Innovations in Power and Advanced Computing Technologies (i-PACT)"},{"key":"10.3233\/IDT-190181_ref8","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/SIBGRAPI.2017.60","article-title":"Cross-database facial expression recognition based on fine-tuned deep convolutional network","author":"Zavarez","year":"2017","journal-title":"2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)"},{"issue":"7","key":"10.3233\/IDT-190181_ref9","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Computation"},{"key":"10.3233\/IDT-190181_ref10","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.3233\/IDT-190181_ref11","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/IDT-190181_ref12","unstructured":"Ioanna-Ourania S, Tsihrintzis GA. 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