{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:46:10Z","timestamp":1761677170374,"version":"3.40.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Programma Operativo Nazionale (PON) - Miur","award":["F\/190066\/01-02\/X44"],"award-info":[{"award-number":["F\/190066\/01-02\/X44"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J of Soc Robotics"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Emotions are an effective communication mode during human\u2013human and human\u2013robot interactions. However, while humans can easily understand other people\u2019s emotions, and they are able to show emotions with natural facial expressions, robot-simulated emotions still represent an open challenge also due to a lack of naturalness and variety of possible expressions. In this direction, we present a two-tier Generative Adversarial Networks (GAN) architecture that generates facial expressions starting from categorical emotions (e.g. joy, sadness, etc.) to obtain a variety of synthesised expressions for each emotion. The proposed approach combines the key features of Conditional Generative Adversarial Networks (CGAN) and GANimation, overcoming their limits by allowing fine modelling of facial expressions, and generating a wide range of expressions for each class (i.e., discrete emotion). The architecture is composed of two modules for generating a synthetic Action Units (AU, i.e., a coding mechanism representing facial muscles and their activation) vector conditioned on a given emotion, and for applying an AU vector to a given image. The overall model is capable of modifying an image of a human face by modelling the facial expression to show a specific discrete emotion. Qualitative and quantitative measurements have been performed to evaluate the ability of the network to generate a variety of expressions that are consistent with the conditioned emotion. Moreover, we also collected people\u2019s responses about the quality and the legibility of the produced expressions by showing them applied to images and a social robot.<\/jats:p>","DOI":"10.1007\/s12369-023-00973-7","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T17:02:51Z","timestamp":1678467771000},"page":"1247-1263","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Two-Tier GAN Architecture for Conditioned Expressions Synthesis on Categorical Emotions"],"prefix":"10.1007","volume":"16","author":[{"given":"Paolo Domenico","family":"Lambiase","sequence":"first","affiliation":[]},{"given":"Alessandra","family":"Rossi","sequence":"additional","affiliation":[]},{"given":"Silvia","family":"Rossi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"973_CR1","doi-asserted-by":"publisher","unstructured":"Rossi A, Dautenhahn K, Koay KL, Walters ML (2020a) How social robots influence people\u2019s trust in critical situations. In: 2020 29th IEEE international conference on robot and human interactive communication (RO-MAN), pp 1020\u20131025. https:\/\/doi.org\/10.1109\/RO-MAN47096.2020.9223471","DOI":"10.1109\/RO-MAN47096.2020.9223471"},{"key":"973_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s12369-020-00650-z","author":"S Rossi","year":"2020","unstructured":"Rossi S, Rossi A, Dautenhahn K (2020) The secret life of robots: perspectives and challenges for robot\u2019s behaviours during non-interactive tasks. Int J Soc Robot. https:\/\/doi.org\/10.1007\/s12369-020-00650-z","journal-title":"Int J Soc Robot"},{"key":"973_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/app10082924","author":"C Filippini","year":"2020","unstructured":"Filippini C, Perpetuini D, Cardone D, Chiarelli AM, Merla A (2020) Thermal infrared imaging-based affective computing and its application to facilitate human robot interaction: a review. Appl Sci. https:\/\/doi.org\/10.3390\/app10082924","journal-title":"Appl Sci"},{"key":"973_CR4","doi-asserted-by":"publisher","unstructured":"Gockley R, Simmons R, Forlizzi J (2006) Modeling affect in socially interactive robots. In: ROMAN 2006\u2014The 15th IEEE international symposium on robot and human interactive communication, pp 558\u2013563 https:\/\/doi.org\/10.1109\/ROMAN.2006.314448","DOI":"10.1109\/ROMAN.2006.314448"},{"issue":"2","key":"973_CR5","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s42235-018-0015-y","volume":"15","author":"F Cavallo","year":"2018","unstructured":"Cavallo F, Semeraro F, Fiorini L, Magyar G, Sin\u010d\u00e1k P, Dario P (2018) Emotion modelling for social robotics applications: a review. J Bionic Eng 15(2):185\u2013203","journal-title":"J Bionic Eng"},{"issue":"2","key":"973_CR6","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s10514-007-9058-3","volume":"24","author":"ML Walters","year":"2008","unstructured":"Walters ML, Syrdal DS, Dautenhahn K, te Boekhorst R, Koay KL (2008) Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion. Auton Robots 24(2):159\u2013178. https:\/\/doi.org\/10.1007\/s10514-007-9058-3","journal-title":"Auton Robots"},{"key":"973_CR7","doi-asserted-by":"publisher","unstructured":"Faria DR, Vieira M, Faria FCC, Premebida C (2017) Affective facial expressions recognition for human-robot interaction. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 805\u2013810. https:\/\/doi.org\/10.1109\/ROMAN.2017.8172395","DOI":"10.1109\/ROMAN.2017.8172395"},{"key":"973_CR8","volume-title":"Advances in neural information processing systems","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. Curran Associates, Inc., Red Hook"},{"key":"973_CR9","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"973_CR10","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Loy CC (2018). Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV) workshops","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"973_CR11","doi-asserted-by":"publisher","unstructured":"Bucci B, Rossi A, Rossi S (2022) Action unit generation through dimensional emotion recognition from text. In: 2022 31st IEEE international conference on robot and human interactive communication (RO-MAN), pp. 1071\u20131076. https:\/\/doi.org\/10.1109\/RO-MAN53752.2022.9900535","DOI":"10.1109\/RO-MAN53752.2022.9900535"},{"key":"973_CR12","doi-asserted-by":"crossref","unstructured":"Pumarola A, Agudo A, Martinez AM, Sanfeliu A, Moreno-Noguer F (2018) Ganimation: anatomically-aware facial animation from a single image. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds), Computer Vision\u2014ECCV 2018, pp 835\u2013851. Springer, Cham","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"973_CR13","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/978-3-030-34356-9_32","volume-title":"High performance computing","author":"A Hesam","year":"2019","unstructured":"Hesam A, Vallecorsa S, Khattak G, Carminati F (2019) Evaluating power architecture for distributed training of generative adversarial networks. In: Weiland M, Juckeland G, Alam S, Jagode H (eds) High performance computing. Springer, Cham, pp 432\u2013440"},{"key":"973_CR14","doi-asserted-by":"crossref","unstructured":"Aneja D, Colburn A, Faigin G, Shapiro L, Mones B (2016) Modeling stylized character expressions via deep learning. In: Asian conference on computer vision, pp 136\u2013153. Springer","DOI":"10.1007\/978-3-319-54184-6_9"},{"issue":"1","key":"973_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/taffc.2017.2740923","volume":"10","author":"A Mollahosseini","year":"2019","unstructured":"Mollahosseini A, Hasani B, Mahoor MH (2019) Affectnet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans Affect Comput 10(1):18\u201331. https:\/\/doi.org\/10.1109\/taffc.2017.2740923","journal-title":"IEEE Trans Affect Comput"},{"key":"973_CR16","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.425"},{"key":"973_CR17","doi-asserted-by":"publisher","first-page":"145","DOI":"10.3389\/frobt.2020.532279","volume":"7","author":"M Spezialetti","year":"2020","unstructured":"Spezialetti M, Placidi G, Rossi S (2020) Emotion recognition for human\u2013robot interaction: recent advances and future perspectives. Front Robot AI 7:145. https:\/\/doi.org\/10.3389\/frobt.2020.532279","journal-title":"Front Robot AI"},{"issue":"1","key":"973_CR18","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1146\/annurev.psych.54.101601.145102","volume":"54","author":"JA Russell","year":"2003","unstructured":"Russell JA, Bachorowski J-A, Fern\u00e1ndez-Dols J-M (2003) Facial and vocal expressions of emotion. Ann Rev Psychol 54(1):329\u2013349. https:\/\/doi.org\/10.1146\/annurev.psych.54.101601.145102","journal-title":"Ann Rev Psychol"},{"issue":"2","key":"973_CR19","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s11263-005-6644-8","volume":"63","author":"P Viola","year":"2005","unstructured":"Viola P, Jones MJ, Snow D (2005) Detecting pedestrians using patterns of motion and appearance. Int J Comput Vis 63(2):153\u2013161","journal-title":"Int J Comput Vis"},{"key":"973_CR20","doi-asserted-by":"publisher","unstructured":"Shao M, Dos\u00a0Reis Alves SF, Ismail O, Zhang X, Nejat G, Benhabib B (2019) You are doing great! only one rep left: an affect-aware social robot for exercising. In: 2019 IEEE international conference on systems, man and cybernetics (SMC), pp 3811\u20133817. https:\/\/doi.org\/10.1109\/SMC.2019.8914198","DOI":"10.1109\/SMC.2019.8914198"},{"key":"973_CR21","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patcog.2015.08.027","volume":"51","author":"DT Nguyen","year":"2016","unstructured":"Nguyen DT, Li W, Ogunbona PO (2016) Human detection from images and videos: a survey. Pattern Recognit 51:148\u2013175. https:\/\/doi.org\/10.1016\/j.patcog.2015.08.027","journal-title":"Pattern Recognit"},{"issue":"3","key":"973_CR22","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/s12369-019-00616-w","volume":"12","author":"S Rossi","year":"2020","unstructured":"Rossi S, Larafa M, Ruocco M (2020) Emotional and behavioural distraction by a social robot for children anxiety reduction during vaccination. Int J Soc Robot 12(3):765\u2013777","journal-title":"Int J Soc Robot"},{"issue":"3","key":"973_CR23","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1075\/is.18066.ros","volume":"20","author":"S Rossi","year":"2019","unstructured":"Rossi S, Ruocco M (2019) Better alone than in bad company: effects of incoherent non-verbal emotional cues for a humanoid robot. Interact Stud 20(3):487\u2013508. https:\/\/doi.org\/10.1075\/is.18066.ros","journal-title":"Interact Stud"},{"issue":"4","key":"973_CR24","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1109\/TCDS.2018.2826921","volume":"10","author":"R Hortensius","year":"2018","unstructured":"Hortensius R, Hekele F, Cross ES (2018) The perception of emotion in artificial agents. IEEE Trans Cognit Develop Syst 10(4):852\u2013864. https:\/\/doi.org\/10.1109\/TCDS.2018.2826921","journal-title":"IEEE Trans Cognit Develop Syst"},{"key":"973_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s12369-022-00867-0","author":"N Rawal","year":"2022","unstructured":"Rawal N, Stock-Homburg RM (2022) Facial emotion expressions in human\u2013robot interaction: a survey. Int J Soc Robot. https:\/\/doi.org\/10.1007\/s12369-022-00867-0","journal-title":"Int J Soc Robot"},{"issue":"1","key":"973_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1109\/TMM.2018.2844085","volume":"21","author":"S Xie","year":"2019","unstructured":"Xie S, Haifeng H (2019) Facial expression recognition using hierarchical features with deep comprehensive multipatches aggregation convolutional neural networks. IEEE Trans Multimed 21(1):211\u2013220. https:\/\/doi.org\/10.1109\/TMM.2018.2844085","journal-title":"IEEE Trans Multimed"},{"key":"973_CR27","doi-asserted-by":"crossref","unstructured":"Ohman A, Lundqvist D, Flykt A (1998) The karolinska directed emotional faces\u2014kdef. Karolinska Institutet, CD ROM from Department of Clinical Neuroscience, Psychology section","DOI":"10.1037\/t27732-000"},{"key":"973_CR28","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1186\/s40064-015-1427-3","volume":"4","author":"P Carcagn\u00ec","year":"2015","unstructured":"Carcagn\u00ec P, Del Coco M, Leo M, Distante C (2015) Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus 4:645. https:\/\/doi.org\/10.1186\/s40064-015-1427-3","journal-title":"SpringerPlus"},{"key":"973_CR29","doi-asserted-by":"publisher","first-page":"4525","DOI":"10.1109\/ACCESS.2017.2676238","volume":"5","author":"Md Zia Uddin","year":"2017","unstructured":"Zia Uddin Md, Hassan MM, Almogren A, Alamri A, Alrubaian M, Fortino G (2017) Facial expression recognition utilizing local direction-based robust features and deep belief network. IEEE Access 5:4525\u20134536. https:\/\/doi.org\/10.1109\/ACCESS.2017.2676238","journal-title":"IEEE Access"},{"key":"973_CR30","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/978-3-030-35888-4_59","volume-title":"Social robotics","author":"C Yu","year":"2019","unstructured":"Yu C, Tapus A (2019) Interactive robot learning for multimodal emotion recognition. In: Salichs MA, Ge SS, Barakova EI, Cabibihan J-J, Wagner AR, Castro-Gonz\u00e1lez \u00c1, He H (eds) Social robotics. Springer, Cham, pp 633\u2013642"},{"issue":"8","key":"973_CR31","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s12369-014-0237-z","volume":"6","author":"C Bennett","year":"2014","unstructured":"Bennett C, Sabanovic S (2014) Deriving minimal features for human-like facial expressions in robotic faces. Int J Soc Robot 6(8):367\u2013381. https:\/\/doi.org\/10.1007\/s12369-014-0237-z","journal-title":"Int J Soc Robot"},{"key":"973_CR32","doi-asserted-by":"publisher","unstructured":"Churamani N, Barros P, Strahl E, Wermter S (2018) Learning empathy-driven emotion expressions using affective modulations. In: 2018 International joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489158","DOI":"10.1109\/IJCNN.2018.8489158"},{"key":"973_CR33","doi-asserted-by":"publisher","first-page":"9848","DOI":"10.1109\/ACCESS.2019.2891668","volume":"7","author":"J Deng","year":"2019","unstructured":"Deng J, Pang G, Zhang Z, Pang Z, Yang H, Yang G (2019) cGAN based facial expression recognition for human\u2013robot interaction. IEEE Access 7:9848\u20139859. https:\/\/doi.org\/10.1109\/ACCESS.2019.2891668","journal-title":"IEEE Access"},{"issue":"3","key":"973_CR34","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.1109\/TII.2020.2991764","volume":"17","author":"D-K Ko","year":"2021","unstructured":"Ko D-K, Lee D-H, Lim S-C (2021) Continuous image generation from low-update-rate images and physical sensors through a conditional gan for robot teleoperation. IEEE Trans Ind Inform 17(3):1978\u20131986. https:\/\/doi.org\/10.1109\/TII.2020.2991764","journal-title":"IEEE Trans Ind Inform"},{"key":"973_CR35","doi-asserted-by":"publisher","unstructured":"Tang H, Wang W, Wu S, Chen X, Xu D, Sebe N, Yan Y (2019) Expression conditional gan for facial expression-to-expression translation. In: 2019 IEEE international conference on image processing (ICIP), pp 4449\u20134453. https:\/\/doi.org\/10.1109\/ICIP.2019.8803654","DOI":"10.1109\/ICIP.2019.8803654"},{"key":"973_CR36","doi-asserted-by":"publisher","unstructured":"Song L, Lu Z, He R, Sun Z, Tan, T (2018) Geometry guided adversarial facial expression synthesis. In: Proceedings of the 26th ACM international conference on multimedia, MM \u201918, pp 627-635, New York, NY, USA, 2018. Association for Computing Machinery. ISBN 9781450356657. https:\/\/doi.org\/10.1145\/3240508.3240612","DOI":"10.1145\/3240508.3240612"},{"key":"973_CR37","doi-asserted-by":"publisher","unstructured":"Geng Z, Cao C, Tulyakov S (2019) 3D guided fine-grained face manipulation. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9813\u20139822, Los Alamitos, CA, USA. IEEE Computer Society. https:\/\/doi.org\/10.1109\/CVPR.2019.01005","DOI":"10.1109\/CVPR.2019.01005"},{"key":"973_CR38","unstructured":"Mirza M, Osindero S (2014). Conditional generative adversarial nets. arXiv:1411.1784"},{"key":"973_CR39","doi-asserted-by":"publisher","unstructured":"Choi Y, Choi M, Kim M, Ha J-W, Kim S, Choo J (2018). Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 8789\u20138797. https:\/\/doi.org\/10.1109\/CVPR.2018.00916","DOI":"10.1109\/CVPR.2018.00916"},{"issue":"3\u20134","key":"973_CR40","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/02699939208411068","volume":"6","author":"P Ekman","year":"1992","unstructured":"Ekman P (1992) An argument for basic emotions. Cognit Emot 6(3\u20134):169\u2013200. https:\/\/doi.org\/10.1080\/02699939208411068","journal-title":"Cognit Emot"},{"issue":"6","key":"973_CR41","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1037\/h0077722","volume":"39","author":"P Ekman","year":"1980","unstructured":"Ekman P, Freisen WV, Ancoli S (1980) Facial signs of emotional experience. J Personal Soc Psychol 39(6):1125","journal-title":"J Personal Soc Psychol"},{"issue":"1","key":"973_CR42","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/BF01115465","volume":"1","author":"P Ekman","year":"1976","unstructured":"Ekman P, Friesen WV (1976) Measuring facial movement. Environ Psychol Nonverbal Behav 1(1):56\u201375","journal-title":"Environ Psychol Nonverbal Behav"},{"key":"973_CR43","doi-asserted-by":"publisher","unstructured":"Mao X, Li Q, Xie H, Lau RK, Wang Z, Smolley S (2017) Least squares generative adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV), pp 2813\u20132821, Los Alamitos, CA, USA. IEEE Computer Society. https:\/\/doi.org\/10.1109\/ICCV.2017.304","DOI":"10.1109\/ICCV.2017.304"},{"key":"973_CR44","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Precup D, Teh YW (eds), Proceedings of the 34th international conference on machine learning, volume\u00a070 of proceedings of machine learning research, pp 214\u2013223. PMLR, 06\u201311 Aug"},{"key":"973_CR45","volume-title":"Advances in neural information processing systems","author":"M Heusel","year":"2017","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local Nash equilibrium. In: Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30. Curran Associates, Inc., Red Hook"},{"key":"973_CR46","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cviu.2018.10.009","volume":"179","author":"A Borji","year":"2019","unstructured":"Borji A (2019) Pros and cons of gan evaluation measures. Comput Vis Image Underst 179:41\u201365. https:\/\/doi.org\/10.1016\/j.cviu.2018.10.009","journal-title":"Comput Vis Image Underst"},{"key":"973_CR47","unstructured":"van\u00a0den Oord A, Kalchbrenner N, Vinyals O, Espeholt L, Graves A, Kavukcuoglu K (2016) Conditional image generation with pixelcnn decoders. In: Proceedings of the 30th international conference on neural information processing systems, NIPS\u201916, pp 4797-4805, Red Hook, NY, USA. Curran Associates Inc. ISBN 9781510838819"},{"key":"973_CR48","unstructured":"Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: Bengio Y, LeCun Y (eds), 4th international conference on learning representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings"},{"key":"973_CR49","unstructured":"Unterthiner T, Nessler B, Seward C, Klambauer G, Heusel M, Ramsauer H, Hochreiter S (2017) Coulomb GANs: provably optimal Nash equilibria via potential fields. arXiv:1708.08819"},{"key":"973_CR50","unstructured":"Ravuri S, Mohamed S, Rosca M, Vinyals O (2018) Learning implicit generative models with the method of learned moments. In: International conference on machine learning, pp 4314\u20134323. PMLR"},{"key":"973_CR51","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016). Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"973_CR52","doi-asserted-by":"crossref","unstructured":"Camras LA (1980) Children\u2019s understanding of facial expressions used during conflict encounters. Child Develop 51(3):879\u2013885","DOI":"10.1111\/j.1467-8624.1980.tb02626.x"},{"key":"973_CR53","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1080\/17470919.2016.1251964","volume":"12","author":"J Chamberland","year":"2016","unstructured":"Chamberland J, Roy-Charland A, Perron M, Dickinson J (2016) Distinction between fear and surprise: an interpretation-independent test of the perceptual-attentional limitation hypothesis. Soc Neurosci 12:10. https:\/\/doi.org\/10.1080\/17470919.2016.1251964","journal-title":"Soc Neurosci"},{"key":"973_CR54","doi-asserted-by":"publisher","unstructured":"Roy-Charland A, Perron M, Beaudry O, Eady K (2014) Confusion of fear and surprise: a test of the perceptual-attentional limitation hypothesis with eye movement monitoring. Cognit Emot 28:01. https:\/\/doi.org\/10.1080\/02699931.2013.878687","DOI":"10.1080\/02699931.2013.878687"},{"issue":"3","key":"973_CR55","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1017\/S0954579405050340","volume":"17","author":"J Posner","year":"2005","unstructured":"Posner J, Russell JA, Peterson BS (2005) The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Develop Psychopathol 17(3):715\u2013734. https:\/\/doi.org\/10.1017\/S0954579405050340","journal-title":"Develop Psychopathol"}],"container-title":["International Journal of Social Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12369-023-00973-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12369-023-00973-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12369-023-00973-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T04:59:50Z","timestamp":1744952390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12369-023-00973-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":55,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["973"],"URL":"https:\/\/doi.org\/10.1007\/s12369-023-00973-7","relation":{},"ISSN":["1875-4791","1875-4805"],"issn-type":[{"type":"print","value":"1875-4791"},{"type":"electronic","value":"1875-4805"}],"subject":[],"published":{"date-parts":[[2023,3,10]]},"assertion":[{"value":"23 January 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}