A multi-channel convolutional neural network based on attention mechanism fusion for facial expression recognition

[1] Keil, A., Miskovic, V. (2015). Human Emotions: AConceptualOverview - ScienceDirect. Sleep and Affect, 23-44. Search in Google Scholar

[2] Anne-Raphalle, Richoz, Junpeng, et al. (2018). Tracking the recognition of static and dynamic facial expressions of emotion across the life span. Journal of Vision, 18(9), 5. Search in Google Scholar

[3] Li, Y.X., Dai, et al. (2017). Giant pandas can discriminate the emotions of human facial pictures. SCI REP-UK, 45-45. Search in Google Scholar

[4] Xueru, Zhao, Xianyou, et al. (2016). Abstract concepts conceptual metaphor theory embodied cognition emotional words weight perception. Frontiers in psychology, 920. Search in Google Scholar

[5] Maria, Kraxenberger, Winfried, et al. (2018). Prosody-Based Sound-Emotion Associations in Poetry. Frontiers in psychology, 1284. Search in Google Scholar

[6] Enrico, Chiovetto, Cristóbal, et al. (2018). Perceptual integration of kinematic components in the recognition of emotional facial expressions. Journal of vision, 18(4), 13, 1–19. Search in Google Scholar

[7] Oksana, Zinchenko, Zachary A., et al. (2018). Brain Responses to Dynamic Facial Expressions: A Normative Meta-Analysis. Frontiers in human neuroscience, 227. Search in Google Scholar

[8] Sylwia, Hyniewska, Wataru, (2018). Cognitive appraisal emotional facial expression naturalistic nonverbal behavior spontaneous expressions. Frontiers in psychology, 2678. Search in Google Scholar

[9] Mauricio R., Papini, Julio C., et al. (2018). Aggression birds comparative psychology conflict emotion fear frustration response suppression. Frontiers in psychology, 2707. Search in Google Scholar

[10] Li, X., Hong, X., Moilanen, A., et al. (2017). Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods. IEEE Transactions on Affective Computing, 563-577. Search in Google Scholar

[11] David, Matsumoto, Hyisung C., et al. (2018). Checkpoints deception facial expressions of emotion microexpressions veracity. Frontiers in psychology, 2545. Search in Google Scholar

[12] Annukka, Lindell. (2018). Lateralization of the expression of facial emotion in humans. Progress in brain research, 249-270. Search in Google Scholar

[13] Hayley, Darke, Simon, et al. (2019). A Novel Dynamic Morphed Stimuli Set to Assess Sensitivity to Identity and Emotion Attributes in Faces. Frontiers in Psychology, 757. Search in Google Scholar

[14] Hess U., Kafetsios K. (2022). Infusing Context Into Emotion Perception Impacts Emotion Decoding Accuracy. Experimental Psychology, 68(6), 285-294. Search in Google Scholar

[15] Reginald B., Adams, Carlos O., et al. (2016). Aging appearance emotional expression face perception%person perception. Frontiers in psychology, 986. Search in Google Scholar

[16] Ke, R., Li, W., Cui, Z., et al. (2019). Two-Stream Multi-Channel Convolutional Neural Network (TMCNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact, 1678. Search in Google Scholar

[17] Khan, A., Sung, J.E., Kang, J. W. (2019). Multi-channel fusion convolutional neural network to classify syntactic anomaly from language-related ERP components. An international journal on information fusion, 53-61. Search in Google Scholar

[18] Taolin, Chen, Keith M., et al. (2016). Dissociable early attentional control mechanisms underlying cognitive and affective conflicts. Scientific reports, 37633. Search in Google Scholar

[19] Mcduff D., Mahmoud, A. N., Mavadati, M., et al. (2016). AFFDEX SDK:A Cross-Platform Real-Time Multi-Face Expression Recognition Toolkit. ACM, 3723-3726. Search in Google Scholar

[20] Paula, F., Christina, N., Ge, C. (2016). Work in Progress 7: REFLEX: Face Micro-Expression Recognition System for TV Content Curation, 163-169. Search in Google Scholar

[21] Jeon, D., Dong, Q., Kim, Y., et al. (2017). A 23-mW Face Recognition Processor with Mostly-Read 5T Memory in 40-nm CMOS. IEEE Journal of Solid-State Circuits, 1628-1642. Search in Google Scholar

[22] Stoychev, S., Gunes, H. (2022). The Effect of Model Compression on Fairness in Facial Expression Recognition, 2201.01709. Search in Google Scholar

[23] Zhang, Z., Wang, M. (2022). Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition, 06673. Search in Google Scholar

[24] Anderer, P., Ross, M., Cerny, A., Fonseca, P., et al. (2022). 0730 Validation studies for scoring polysomnograms and home sleep apnea tests with artificial intelligence: Sleep stage probabilities (hypnodensity) derived from neurological or cardiorespiratory signals. Sleep, 45(Supplement_1), A319-A319. Search in Google Scholar

[25] Yu, Jin, Cha, et al. (2018). Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks. Anticancer research, 5437-5445. Search in Google Scholar

[26] Saraiva, M. J.. M, Macedo, G., Ribeiro, T., et al. (2022). ARTIFICIAL INTELLIGENCE AND CAPSULE ENDOSCOPY: AUTOMATIC CLASSIFICATION OF SMALL BOWEL PREPARATION USING A CONVOLUTIONAL NEURAL NETWORK. Endoscopy, 29(5), 331-338. Search in Google Scholar

[27] Horani, M. O., Najeeb, M., Saeed, A. (2021). Model electric car with wireless charging using solar energy. 3c Tecnología: glosas de innovación aplicadas a la pyme, 10(4), 89-101. Search in Google Scholar