{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:09:57Z","timestamp":1771456197241,"version":"3.50.1"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1109\/ipta.2019.8936114","type":"proceedings-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:58:51Z","timestamp":1576803531000},"page":"1-6","source":"Crossref","is-referenced-by-count":25,"title":["MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images"],"prefix":"10.1109","author":[{"given":"Ilke","family":"Cugu","sequence":"first","affiliation":[]},{"given":"Eren","family":"Sener","sequence":"additional","affiliation":[]},{"given":"Emre","family":"Akbas","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref38","first-page":"1","article-title":"Au-aware deep networks for facial expression recognition","author":"liu","year":"2013","journal-title":"FG"},{"key":"ref33","article-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016"},{"key":"ref32","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"AISTATS"},{"key":"ref31","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"ref37","article-title":"From facial expression recognition to interpersonal relation prediction","author":"zhang","year":"2016"},{"key":"ref36","first-page":"143","article-title":"Deeply learning deformable facial action parts model for dynamic expression analysis","author":"liu","year":"2014","journal-title":"ACCV"},{"key":"ref35","author":"ekman","year":"1997","journal-title":"What the Face Reveals Basic and Applied Studies of Spontaneous Expression using the Facial Action Coding System (FACS)"},{"key":"ref34","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"JMLR"},{"key":"ref10","article-title":"Learning active facial patches for expression analysis","author":"zhong","year":"2012","journal-title":"CVPR"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.233"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2016.7477450"},{"key":"ref13","article-title":"Deep generative-contrastive networks for facial expression recognition","author":"kim","year":"2017"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref15","article-title":"Do deep nets really need to be deep?","author":"ba","year":"2014","journal-title":"NeurIPS"},{"key":"ref16","first-page":"6","article-title":"Deep face recognition","volume":"1","author":"parkhi","year":"2015","journal-title":"BMVC"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2818346.2830587"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(95)00067-4"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33709-3_45"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2011.07.002"},{"key":"ref27","article-title":"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and&#x00A1; 0.5 mb model size","author":"iandola","year":"2016"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"ref6","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015"},{"key":"ref29","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"ICML"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1134\/S1054661807040190"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.297"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2017.23"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33868-7_25"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_27"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/s130607714"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.602"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2013.6475006"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2818346.2830596"},{"key":"ref23","first-page":"1749","article-title":"Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition","author":"liu","year":"2014","journal-title":"CVPR"},{"key":"ref26","article-title":"Fitnets: Hints for thin deep nets","author":"romero","year":"2014"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.341"}],"event":{"name":"2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)","location":"Istanbul, Turkey","start":{"date-parts":[[2019,11,6]]},"end":{"date-parts":[[2019,11,9]]}},"container-title":["2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8932637\/8936068\/08936114.pdf?arnumber=8936114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T15:12:26Z","timestamp":1658157146000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8936114\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/ipta.2019.8936114","relation":{},"subject":[],"published":{"date-parts":[[2019,11]]}}}