{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:58:36Z","timestamp":1782989916532,"version":"3.54.5"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s11042-020-09451-y","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T07:02:53Z","timestamp":1598338973000},"page":"83-125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A comparative study of Autistic Children Emotion recognition based on Spatio-Temporal and Deep analysis of facial expressions features during a Meltdown Crisis"],"prefix":"10.1007","volume":"80","author":[{"given":"Salma Kammoun","family":"Jarraya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5248-8525","authenticated-orcid":false,"given":"Marwa","family":"Masmoudi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Hammami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,8,25]]},"reference":[{"key":"9451_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed W, Mitra S, Chanda K, Mazumdar D (2013) Assisting the autistic with improved facial expression recognition from mixed expressions. In: 2013 Fourth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG). IEEE, pp 1\u20134","DOI":"10.1109\/NCVPRIPG.2013.6776229"},{"key":"9451_CR2","unstructured":"Alaoui AEK (2017) Math\u00e9matiques pour l\u2019enseignement: master 1. MEEF Ellipses"},{"key":"9451_CR3","unstructured":"Anwar S, Milanova M (2016) Real time face expression recognition of children with autism. In: IAEMR"},{"key":"9451_CR4","unstructured":"Application FB (2016) https:\/\/developer.microsoft.com\/en-us\/windows\/Kinect"},{"key":"9451_CR5","unstructured":"Bennie M (2016) Tantrum vs autistic meltdown: What is the difference. Autism Awareness"},{"key":"9451_CR6","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"9451_CR7","unstructured":"CIM-10: (2008). https:\/\/icd.who.int\/browse10\/2008\/fr#\/I"},{"key":"9451_CR8","unstructured":"Control D (2018) Prevention. https:\/\/www.cdc.gov\/"},{"key":"9451_CR9","unstructured":"Dettmers T (2019) A full hardware guide to deep learning"},{"key":"9451_CR10","unstructured":"Documentation KS (2016) https:\/\/docs.microsoft.com\/en-us\/previous-versions\/windows\/kinect\/"},{"key":"9451_CR11","unstructured":"DSM-V (2013) http:\/\/www.psychomedia.qc.ca\/dsm-5\/2013-05-22\/guide-psychomedia"},{"issue":"4","key":"9451_CR12","doi-asserted-by":"publisher","first-page":"443","DOI":"10.31887\/DCNS.2015.17.4\/sdu","volume":"17","author":"S Du","year":"2015","unstructured":"Du S, Martinez AM (2015) Compound facial expressions of emotion: from basic research to clinical applications. Dialog Clin Neurosci 17(4):443","journal-title":"Dialog Clin Neurosci"},{"issue":"15","key":"9451_CR13","doi-asserted-by":"publisher","first-page":"E1454","DOI":"10.1073\/pnas.1322355111","volume":"111","author":"S Du","year":"2014","unstructured":"Du S, Tao Y, Martinez AM (2014) Compound facial expressions of emotion. Proc Natl Acad Sci 111(15):E1454\u2013E1462","journal-title":"Proc Natl Acad Sci"},{"key":"9451_CR14","doi-asserted-by":"crossref","unstructured":"Durmus\u0307o\u0121lu A, Kahraman Y (2016) Facial expression recognition using geometric features. In: 2016 International conference on systems, signals and image processing (IWSSIP). IEEE, pp 1\u20135","DOI":"10.1109\/IWSSIP.2016.7502700"},{"key":"9451_CR15","doi-asserted-by":"crossref","unstructured":"Ekman P (2009) Lie catching and microexpressions. The philosophy of deception, pp 118\u2013133","DOI":"10.1093\/acprof:oso\/9780195327939.003.0008"},{"key":"9451_CR16","unstructured":"Eyes F (2018) http:\/\/comprendrelautisme.com\/lautisme\/les-signes-de-lautisme\/"},{"key":"9451_CR17","unstructured":"Grimaces of anger, S (2018). https:\/\/www.cairn.info"},{"key":"9451_CR18","doi-asserted-by":"crossref","unstructured":"Guha T, Yang Z, Ramakrishna A, Grossman RB, Hedley D, Lee S, Narayanan SS (2015) On quantifying facial expression-related atypicality of children with autism spectrum disorder. In: 2015 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 803\u2013807","DOI":"10.1109\/ICASSP.2015.7178080"},{"key":"9451_CR19","doi-asserted-by":"publisher","first-page":"26391","DOI":"10.1109\/ACCESS.2018.2831927","volume":"6","author":"J Guo","year":"2018","unstructured":"Guo J, Lei Z, Wan J, Avots E, Hajarolasvadi N, Knyazev B, Kuharenko A, Junior JCSJ, Bar\u00f3 X, Demirel H et al (2018) Dominant and complementary emotion recognition from still images of faces. IEEE Access 6:26391\u201326403","journal-title":"IEEE Access"},{"issue":"6","key":"9451_CR20","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/TKDE.2003.1245283","volume":"15","author":"MA Hall","year":"2003","unstructured":"Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15(6):1437\u20131447","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"9451_CR21","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newslett 11(1):10\u201318","journal-title":"ACM SIGKDD Explor Newslett"},{"key":"9451_CR22","doi-asserted-by":"crossref","unstructured":"Haque MIU, Valles D (2018) A facial expression recognition approach using dcnn for autistic children to identify emotions. In: 2018 IEEE 9Th annual information technology, electronics and mobile communication conference (IEMCON). IEEE, pp 546\u2013551","DOI":"10.1109\/IEMCON.2018.8614802"},{"key":"9451_CR23","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9451_CR24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, pp 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"9451_CR25","doi-asserted-by":"crossref","unstructured":"Hira ZM, Gillies DF (2015) A review of feature selection and feature extraction methods applied on microarray data Advances in bioinformatics 2015","DOI":"10.1155\/2015\/198363"},{"key":"9451_CR26","unstructured":"Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1. IEEE, pp 278\u2013282"},{"key":"9451_CR27","unstructured":"Howard A, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"9451_CR28","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"9451_CR29","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and 0.5 mb model size. arXiv:1602.07360"},{"key":"9451_CR30","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167"},{"key":"9451_CR31","doi-asserted-by":"crossref","unstructured":"Jaiswal S, Valstar MF, Gillott A, Daley D (2017) Automatic detection of adhd and asd from expressive behaviour in rgbd data. In: 2017 12Th IEEE international conference on automatic face & gesture recognition (FG 2017). IEEE, pp 762\u2013769","DOI":"10.1109\/FG.2017.95"},{"key":"9451_CR32","unstructured":"Karkra JS, Kaur J (2016) Compound facial expression recognition through gabor filter and rbf network. International Journal of Computer Science and Mobile Computing, pp 576\u2013583"},{"key":"9451_CR33","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"3","key":"9451_CR34","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1023\/A:1006641104653","volume":"24","author":"D LaPlante","year":"2000","unstructured":"LaPlante D, Ambady N (2000) Multiple messages: Facial recognition advantage for compound expressions. J Nonverbal Behav 24(3):211\u2013224","journal-title":"J Nonverbal Behav"},{"key":"9451_CR35","first-page":"276","volume":"261","author":"Y LeCun","year":"1995","unstructured":"LeCun Y, Jackel L, Bottou L, Cortes C, Denker JS, Drucker H, Guyon I, Muller UA, Sackinger E, Simard P et al (1995) Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Netw Stat Mech Perspect 261:276","journal-title":"Neural Netw Stat Mech Perspect"},{"issue":"7553","key":"9451_CR36","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"9451_CR37","doi-asserted-by":"crossref","unstructured":"Liawatimena S, Heryadi Y, Trisetyarso A, Wibowo A, Abbas BS, Barlian E, et al. (2018) A fish classification on images using transfer learning and matlab. In: 2018 Indonesian association for pattern recognition international conference (INAPR). IEEE, pp 108\u2013112","DOI":"10.1109\/INAPR.2018.8627007"},{"key":"9451_CR38","first-page":"3159","volume":"118","author":"D Liliana","year":"2018","unstructured":"Liliana D, Basaruddin T, Widyanto M (2018) Mixed facial emotion recognition using active appearance model and hidden conditional random fields. Int J Pure Appl Math 118:3159\u20133167","journal-title":"Int J Pure Appl Math"},{"key":"9451_CR39","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision. Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"9451_CR40","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.copsyc.2017.06.009","volume":"17","author":"AM Martinez","year":"2017","unstructured":"Martinez AM (2017) Visual perception of facial expressions of emotion. Curr Opin Psychol 17:27\u201333","journal-title":"Curr Opin Psychol"},{"key":"9451_CR41","doi-asserted-by":"crossref","unstructured":"Masmoudi M, Jarraya SK, Hammami M (2019) Meltdowncrisis: Dataset of autistic children during meltdown crisis. In: 2019 15Th international conference on signal-image technology & internet-based systems (SITIS). IEEE, pp 239\u2013246","DOI":"10.1109\/SITIS.2019.00048"},{"issue":"6","key":"9451_CR42","doi-asserted-by":"publisher","first-page":"9246","DOI":"10.1109\/JIOT.2019.2896151","volume":"6","author":"I Mehmood","year":"2019","unstructured":"Mehmood I, Ullah A, Muhammad K, Deng DJ, Meng W, Al-Turjman F, Sajjad M, De Albuquerque VHC (2019) Efficient image recognition and retrieval on iot-assisted energy-constrained platforms from big data repositories. IEEE Internet Things J 6(6):9246\u20139255","journal-title":"IEEE Internet Things J"},{"key":"9451_CR43","unstructured":"or crying by closing the eyes H (2018) https:\/\/www.asperansa.org\/prob\/_sensoriels.html"},{"key":"9451_CR44","unstructured":"or crying by closing the eyes H open the mouth: (2018). http:\/\/www.siwadam.com\/hmm\/a3.html"},{"key":"9451_CR45","unstructured":"Powers DM (2011) Evaluation: from precision, recall and f-measure to roc. informedness markedness and correlation"},{"key":"9451_CR46","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"9451_CR47","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:1804.02767"},{"issue":"19","key":"9451_CR48","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys Y, Inza I, Larra\u00f1aga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507\u20132517","journal-title":"Bioinformatics"},{"key":"9451_CR49","doi-asserted-by":"crossref","unstructured":"Sainath TN, Vinyals O, Senior A, Sak H (2015) Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 4580\u20134584","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"9451_CR50","doi-asserted-by":"crossref","unstructured":"Sajjad M, Zahir S, Ullah A, Akhtar Z, Muhammad K (2019) Human behavior understanding in big multimedia data using cnn based facial expression recognition. Mob Netw Appl:1\u201311","DOI":"10.1007\/s11036-019-01366-9"},{"issue":"1","key":"9451_CR51","first-page":"52","volume":"8","author":"FZ Salmam","year":"2018","unstructured":"Salmam FZ, Madani A, Kissi M (2018) Emotion recognition from facial expression based on fiducial points detection and using neural network. Int J Electr Comput Eng 8(1):52","journal-title":"Int J Electr Comput Eng"},{"key":"9451_CR52","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"9451_CR53","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"9451_CR54","unstructured":"Smiles N (2018) http:\/\/www.autistessansfrontieres.com\/lenfant-autiste-ou-tsa\/"},{"key":"9451_CR55","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. arXiv:1505.00387"},{"key":"9451_CR56","doi-asserted-by":"crossref","unstructured":"Suk M, Prabhakaran B (2014) Real-time mobile facial expression recognition system-a case study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 132\u2013137","DOI":"10.1109\/CVPRW.2014.25"},{"key":"9451_CR57","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9451_CR58","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"9451_CR59","doi-asserted-by":"crossref","unstructured":"Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A, Le QV (2019) Mnasnet: Platform-aware neural architecture search for mobile. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2820\u20132828","DOI":"10.1109\/CVPR.2019.00293"},{"key":"9451_CR60","unstructured":"Tasnim N, Khwaja A, Rashid H (2018) Emotion recognition from facial expression of autism spectrum disordered children using image processing and machine learning algorithms. Ph.D. thesis, BRAC University"},{"key":"9451_CR61","unstructured":"Villacampa O (2015) Feature selection and classification methods for decision making: a comparative analysis"},{"key":"9451_CR62","doi-asserted-by":"crossref","unstructured":"Wang S, Chen CS, Rinsurongkawong V, Akdag F, Eick CF (2010) A polygon-based methodology for mining related spatial datasets. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on data mining for geoinformatics. ACM, pp 1\u20138","DOI":"10.1145\/1869890.1869891"},{"key":"9451_CR63","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp 818\u2013833","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"9451_CR64","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.knosys.2014.03.015","volume":"64","author":"Y Zhang","year":"2014","unstructured":"Zhang Y, Wang S, Phillips P, Ji G (2014) Binary pso with mutation operator for feature selection using decision tree applied to spam detection. Knowl-Based Syst 64:22\u201331","journal-title":"Knowl-Based Syst"},{"key":"9451_CR65","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"9451_CR66","doi-asserted-by":"crossref","unstructured":"Zhao L, Wang Z, Zhang G (2017) Facial expression recognition from video sequences based on spatial-temporal motion local binary pattern and gabor multiorientation fusion histogram. Mathematical Problems in Engineering 2017","DOI":"10.1155\/2017\/7206041"},{"key":"9451_CR67","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognitin, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09451-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09451-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09451-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T00:12:05Z","timestamp":1629850325000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09451-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,25]]},"references-count":67,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9451"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09451-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,25]]},"assertion":[{"value":"29 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}