{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T04:39:47Z","timestamp":1761194387385,"version":"build-2065373602"},"reference-count":92,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIN\/AEI","award":["PID2022-140554OB-C32","PID2021-124335OB-C22"],"award-info":[{"award-number":["PID2022-140554OB-C32","PID2021-124335OB-C22"]}]},{"DOI":"10.13039\/100012818","name":"Comunidad de Madrid","doi-asserted-by":"publisher","award":["TEC-2024\/ECO-277"],"award-info":[{"award-number":["TEC-2024\/ECO-277"]}],"id":[{"id":"10.13039\/100012818","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Driver emotion recognition is vital for intelligent driver assistance systems, where the accurate detection of emotional states enhances both safety and user experience. Current approaches, however, require extensive labeled datasets, perform poorly under real-world conditions, and degrade with class imbalance. To overcome these challenges, we propose the Active Learning and Deep Attention Mechanism (ALDAM) framework. ALDAM introduces three key innovations: (1) an active learning cycle that reduces labeling effort by ~40%; (2) a weighted-cluster loss that mitigates class imbalance; and (3) a deep attention mechanism that strengthens feature selection under occlusion, pose variation, and illumination changes. Evaluated on four benchmark datasets (FER-2013, AffectNet, CK+, and EMOTIC), ALDAM achieves an average accuracy of 97.58%, F1-score of 98.64%, and AUC of 98.76% surpassing CNN-based models and advanced baselines such as SE-ResNet-50. These results establish ALDAM as a robust and efficient solution for real-time driver emotion recognition.<\/jats:p>","DOI":"10.3390\/a18100669","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:53:19Z","timestamp":1761058399000},"page":"669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Active Learning and Deep Attention Framework for Robust Driver Emotion Recognition"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8439-467X","authenticated-orcid":false,"given":"Bashar Sami Nayyef","family":"Al-dabbagh","sequence":"first","affiliation":[{"name":"Computer Science & Engineering Department, Universidad Carlos III de Madrid, 28911 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0041-6829","authenticated-orcid":false,"given":"Agapito","family":"Ledezma Espino","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering Department, Universidad Carlos III de Madrid, 28911 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1429-4092","authenticated-orcid":false,"given":"Araceli Sanchis de","family":"Miguel","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering Department, Universidad Carlos III de Madrid, 28911 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hickson, S., Dufour, N., Sud, A., Kwatra, V., and Essa, I. (2019, January 7\u201311). Eyemotion: Classifying facial expressions in VR using eye-tracking cameras. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2019.00178"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.ridd.2014.10.015","article-title":"Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders","volume":"36","author":"Chen","year":"2015","journal-title":"Res. Dev. Disabil."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ngo, Q.T., and Yoon, S. (2020). Facial expression recognition based on weighted-cluster loss and deep transfer learning using a highly imbalanced dataset. Sensors, 20.","DOI":"10.3390\/s20092639"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"542918","DOI":"10.1155\/2008\/542918","article-title":"A real-time facial expression recognition system for online games","volume":"2008","author":"Zhan","year":"2008","journal-title":"Int. J. Comput. Games Technol."},{"key":"ref_5","unstructured":"Wang, J., and Gong, Y. (2008, January 8\u201311). Recognition of multiple drivers\u2019 emotional state. Proceedings of the 19th International Conference on Pattern Recognition (ICPR), Tampa, FL, USA."},{"key":"ref_6","first-page":"142","article-title":"Determinants of risky driving behavior: A narrative review","volume":"28","author":"Jafarpour","year":"2014","journal-title":"Med. J. Islam. Repub. Iran"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trf.2017.06.019","article-title":"Emotions, behaviour, and the adolescent driver: A literature review","volume":"50","year":"2017","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100506","DOI":"10.1016\/j.measen.2022.100506","article-title":"A deep learning-based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer disease using MRI scans","volume":"24","author":"Sharma","year":"2022","journal-title":"Meas. Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mathew, A., Amudha, P., and Sivakumari, S. (2020). Deep learning techniques: An overview. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, Springer.","DOI":"10.1007\/978-981-15-3383-9_54"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"119957","DOI":"10.1016\/j.eswa.2023.119957","article-title":"Efficient neural architecture search for emotion recognition","volume":"224","author":"Verma","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"El Boudouri, Y., and Bohi, A. (2023, January 27\u201329). Emonext: An adapted ConvNeXt for facial emotion recognition. Proceedings of the 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), Poitiers, France.","DOI":"10.1109\/MMSP59012.2023.10337732"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kopalidis, T., Solachidis, V., Vretos, N., and Daras, P. (2024). Advances in facial expression recognition: A survey of methods, benchmarks, models, and datasets. Information, 15.","DOI":"10.3390\/info15030135"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.aej.2023.01.017","article-title":"A comprehensive survey on deep facial expression recognition: Challenges, applications, and future guidelines","volume":"68","author":"Sajjad","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s11554-023-01310-x","article-title":"Few-shot learning for facial expression recognition: A comprehensive survey","volume":"20","author":"Kim","year":"2023","journal-title":"J. Real-Time Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhang, T., Mao, Q., Duan, L., and Xu, C. (2018, January 22\u201326). Facial expression recognition in the wild: A cycle-consistent adversarial attention transfer approach. Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Republic of Korea.","DOI":"10.1145\/3240508.3240574"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, H., Liang, J., and Jiang, D. (2025). Occlusion-robust facial expression recognition based on multi-angle feature extraction. Appl. Sci., 15.","DOI":"10.3390\/app15095139"},{"key":"ref_18","unstructured":"Tian, Y.L., Kanade, T., and Cohn, J.F. (2002, January 20\u201321). Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity. Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR), Washington, DC, USA."},{"key":"ref_19","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 Vis. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dahmane, M., and Meunier, J. (2011, January 21\u201323). Emotion recognition using dynamic grid-based HoG features. Proceedings of the 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG), Santa Barbara, CA, USA.","DOI":"10.1109\/FG.2011.5771368"},{"key":"ref_21","first-page":"68","article-title":"Deep learning and face recognition: The state of the art","volume":"9457","author":"Balaban","year":"2015","journal-title":"Proc. SPIE Biometric Surveill. Technol. Human Act. Identif. XII"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/CVPR.2005.177","article-title":"Histograms of oriented gradients for human detection","volume":"Volume 1","author":"Dalal","year":"2005","journal-title":"Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1109\/34.541406","article-title":"Image representation using 2D Gabor wavelets","volume":"18","author":"Lee","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","unstructured":"Whitehill, J., and Omlin, C.W. (2006, January 10\u201312). Haar features for FACS AU recognition. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shafiq, M., and Gu, Z. (2022). Deep residual learning for image recognition: A survey. Appl. Sci., 12.","DOI":"10.3390\/app12188972"},{"key":"ref_29","first-page":"6015","article-title":"Sika deer facial recognition model based on SE-ResNet","volume":"72","author":"Gong","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, H., Hu, H., Jin, Z., Xu, Y., and Liu, X. (2025, January 23\u201325). The image recognition and classification model based on ConvNeXt for intelligent arms. Proceedings of the 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China.","DOI":"10.1109\/ICETCI64844.2025.11084094"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yuen, K., Martin, S., and Trivedi, M.M. (2016, January 4\u20138). On looking at faces in an automobile: Issues, algorithms and evaluation on a naturalistic driving dataset. Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7900056"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1109\/TPAMI.2019.2914680","article-title":"Deep imbalanced learning for face recognition and attribute prediction","volume":"42","author":"Huang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","unstructured":"LeCun, Y., Denker, J., and Solla, S. (1989). Optimal brain damage. Adv. Neural Inf. Process. Syst., 2, Available online: https:\/\/proceedings.neurips.cc\/paper\/1989\/hash\/6c9882bbac1c7093bd25041881277658-Abstract.html."},{"key":"ref_34","first-page":"1293","article-title":"Automatic attendance system based on CNN\u2013LSTM and face recognition","volume":"16","author":"Shukla","year":"2024","journal-title":"Int. J. Inf. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gao, M., Zhang, Z., Yu, G., Ar\u0131k, S.\u00d6., Davis, L.S., and Pfister, T. (2020). Consistency-based semi-supervised active learning: Towards minimizing labeling cost. European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-58607-2_30"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.jksuci.2018.09.002","article-title":"A survey on human face expression recognition techniques","volume":"33","author":"Revina","year":"2021","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"11523","DOI":"10.1109\/ACCESS.2024.3440631","article-title":"Short-term load forecasting: A comprehensive review and simulation study with CNN-LSTM hybrids approach","volume":"12","author":"Ullah","year":"2024","journal-title":"IEEE Access"},{"key":"ref_39","first-page":"1","article-title":"Active learning literature survey","volume":"1648","author":"Settles","year":"2009","journal-title":"Univ. Wisconsin\u2013Madison Tech. Rep."},{"key":"ref_40","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Ma, L., Jiang, H., and Zhao, H. (2017, January 23\u201328). Deep residual networks for hyperspectral image classification. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127330"},{"key":"ref_42","first-page":"464","article-title":"Very high resolution images classification by fine tuning deep convolutional neural networks","volume":"Volume 10033","author":"Iftene","year":"2016","journal-title":"Proceedings of the Eighth International Conference on Digital Image Processing (ICDIP 2016)"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s40687-022-00370-y","article-title":"Deep limits of residual neural networks","volume":"10","author":"Thorpe","year":"2023","journal-title":"Res. Math. Sci."},{"key":"ref_44","unstructured":"Targ, S., Almeida, D., and Lyman, K. (2016). ResNet in ResNet: Generalizing residual architectures. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108384","DOI":"10.1016\/j.compeleceng.2022.108384","article-title":"A ResNet deep learning based facial recognition design for future multimedia applications","volume":"104","author":"Durga","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Koonce, B. (2021). ResNet 50. Convolutional Neural Networks with Swift for TensorFlow: Image Recognition and Dataset Categorization, Apress.","DOI":"10.1007\/978-1-4842-6168-2"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1109\/TAI.2023.3299903","article-title":"Modified ResNet-152 network with hybrid pyramidal pooling for local change detection","volume":"5","author":"Panda","year":"2023","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Demir, A., Yilmaz, F., and Kose, O. (2019). Early detection of skin cancer using deep learning architectures: ResNet-101 and Inception-V3. 2019 Medical Technologies Congress (TIPTEKNO), Izmir, Turkey, 3\u20135 October 2019, IEEE.","DOI":"10.1109\/TIPTEKNO47231.2019.8972045"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1109\/TASL.2013.2284378","article-title":"Optimization techniques to improve training speed of deep neural networks for large speech tasks","volume":"21","author":"Sainath","year":"2013","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_50","first-page":"1","article-title":"A survey of deep active learning","volume":"54","author":"Ren","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_51","unstructured":"Gal, Y., Islam, R., and Ghahramani, Z. (2017, January 6\u201311). Deep Bayesian active learning with image data. Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia."},{"key":"ref_52","unstructured":"Kirsch, A., van Amersfoort, J., and Gal, Y. (2019). BatchBALD: Efficient and diverse batch acquisition for deep Bayesian active learning. Adv. Neural Inf. Process. Syst., 32, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/hash\/95323660ed2124450caaac2c46b5ed90-Abstract.html."},{"key":"ref_53","unstructured":"Sener, O., and Savarese, S. (2017). Active learning for convolutional neural networks: A core-set approach. arXiv."},{"key":"ref_54","unstructured":"Ash, J.T., Zhang, C., Krishnamurthy, A., Langford, J., and Agarwal, A. (2019). Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.1109\/JBHI.2021.3052320","article-title":"DSAL: Deeply supervised active learning from strong and weak labelers for biomedical image segmentation","volume":"25","author":"Zhao","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_56","unstructured":"Zhang, C., and Chaudhuri, K. (2015). Active learning from weak and strong labelers. Adv. Neural Inf. Process. Syst., 28, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2015\/hash\/eba0dc302bcd9a273f8bbb72be3a687b-Abstract.html."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1561\/2200000037","article-title":"Theory of disagreement-based active learning","volume":"7","author":"Hanneke","year":"2014","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_58","unstructured":"Tosh, C.J., and Hsu, D. (2022, January 17\u201323). Simple and near-optimal algorithms for hidden stratification and multi-group learning. Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, USA."},{"key":"ref_59","first-page":"4260","article-title":"Contrastive active learning under class distribution mismatch","volume":"45","author":"Du","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_60","first-page":"1655","article-title":"Active learning with feedback on features and instances","volume":"7","author":"Raghavan","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Shah, N.A., Safaei, B., Sikder, S., Vedula, S.S., and Patel, V.M. (2025, January 6\u201310). StepAL: Step-aware active learning for cataract surgical videos. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Marrakesh, Morocco.","DOI":"10.1007\/978-3-032-05114-1_53"},{"key":"ref_62","unstructured":"Ildiz, M.E., Huang, Y., Li, Y., Rawat, A.S., and Oymak, S. (2024). From self-attention to Markov models: Unveiling the dynamics of generative transformers. arXiv."},{"key":"ref_63","unstructured":"Makkuva, A.V., Bondaschi, M., Girish, A., Nagle, A., Jaggi, M., Kim, H., and Gastpar, M. (2024). Attention with Markov: A framework for principled analysis of transformers via Markov chains. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, A., Ouadou, A., Max, H., Duan, Y., Tanner, J.J., and Cheng, J. (2020). DeepCryoPicker: Fully automated deep neural network for single protein particle picking in cryo-EM. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-03809-7"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, A., Ouadou, A., Tanner, J.J., and Cheng, J. (2019). AutoCryoPicker: An unsupervised learning approach for fully automated single particle picking in cryo-EM images. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2926-y"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, A., Ouadou, A., Tanner, J.J., and Cheng, J. (2019). A super-clustering approach for fully automated single particle picking in cryo-EM. Genes, 10.","DOI":"10.1101\/561928"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s11227-025-07008-0","article-title":"Optimizing web page retrieval performance with advanced query expansion: Leveraging ChatGPT and metadata-driven analysis","volume":"81","author":"Alani","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, A. (2023). Deep semantic segmentation-based unlabeled positive CNN\u2019s loss function for fully automated human finger vein identification. AIP Conference Proceedings, AIP Publishing.","DOI":"10.1063\/5.0164356"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Chowdhary, K. (2020). Natural language processing. Fundamentals of Artificial Intelligence, Springer.","DOI":"10.1007\/978-81-322-3972-7"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","article-title":"AffectNet: A database for facial expression, valence, and arousal computing in the wild","volume":"10","author":"Mollahosseini","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., and Matthews, I. (2010, January 13\u201318). The extended Cohn\u2013Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\u2014Workshops (CVPRW), San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Goodfellow, I.J., Erhan, D., Carrier, P.-L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., and Lee, D.-H. (2013, January 3\u20137). Challenges in representation learning: A report on three machine learning contests. Proceedings of the International Conference on Neural Information Processing (ICONIP), Daegu, Republic of Korea.","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Kosti, R., Alvarez, J.M., Recasens, A., and Lapedriza, A. (2017, January 21\u201326). Emotic: Emotions in context dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.285"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Azzawi, A.A., and Al-Saedi, M.A. (2010, January 6\u20138). Face recognition based on mixed between selected feature by multiwavelet and particle swarm optimization. Proceedings of the 2010 Developments in e-Systems Engineering (DeSE), London, UK.","DOI":"10.1109\/DeSE.2010.39"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, A., Hind, J., and Cheng, J. (2018, January 2\u20135). Localized deep-CNN structure for face recognition. Proceedings of the 2018 11th International Conference on Developments in eSystems Engineering (DeSE), Cambridge, UK.","DOI":"10.1109\/DeSE.2018.00049"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, A., Al-Sadr, H., Cheng, J., and Han, T.X. (2018, January 17\u201320). Localized Deep Norm-CNN structure for face verification. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00010"},{"key":"ref_79","first-page":"4","article-title":"Deep learning approach for secondary structure protein prediction based on first level features extraction using a latent CNN structure","volume":"8","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_80","first-page":"1476","article-title":"Secondary structure protein prediction-based first level features extraction using U-Net and sparse auto-encoder","volume":"9","author":"Alsaedi","year":"2025","journal-title":"JOIV Int. J. Inform. Vis."},{"key":"ref_81","first-page":"2025058","article-title":"Fully automated unsupervised learning approach for thermal camera calibration and an accurate COVID-19 human temperature tracking","volume":"7","author":"Hussein","year":"2025","journal-title":"Multidiscip. Sci. J."},{"key":"ref_82","first-page":"2025065","article-title":"Fully automated real-time approach for human temperature prediction and COVID-19 detection-based thermal skin face extraction using deep semantic segmentation","volume":"7","author":"Hussein","year":"2025","journal-title":"Multidiscip. Sci. J."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2024156","DOI":"10.31893\/multiscience.2024156","article-title":"Breast cancer prediction: A CNN approach","volume":"6","author":"Thamir","year":"2024","journal-title":"Multidiscip. Sci. J."},{"key":"ref_84","first-page":"775","article-title":"A review of the single-stage vs. two-stage detectors algorithm: Comprehensive insights into object detection","volume":"11","author":"Alhashmi","year":"2025","journal-title":"Int. J. Environ. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Alani, A.A., and Al-Azzawia, A. (2025, January 15\u201316). Design a secure customize search engine based on link\u2019s metadata analysis. Proceedings of the 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Tangier, Morocco.","DOI":"10.1109\/IRASET64571.2025.11008284"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Gao, R., Lu, H., Al-Azzawi, A., Li, Y., and Zhao, C. (2023). DRL-FVRestore: An adaptive selection and restoration method for finger vein images based on deep reinforcement. Appl. Sci., 13.","DOI":"10.3390\/app13020699"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"129717","DOI":"10.1016\/j.neucom.2025.129717","article-title":"Spore: Spatio-temporal collaborative perception and representation space disentanglement for remote heart rate measurement","volume":"630","author":"Zhang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_88","unstructured":"Al-Azzawi, A. (2025, September 27). An efficient spatially invariant model for fingerprint authentication based on particle swarm optimization. Unpubl. Manuscr., Available online: https:\/\/www.researchgate.net\/publication\/322499068_An_Efficient_Spatially_Invariant_Model_for_Fingerprint_Authentication_based_on_Particle_Swarm_Optimization."},{"key":"ref_89","first-page":"618","article-title":"NLP-based sentiment analysis for Twitter\u2019s opinion mining and visualization","volume":"Volume 11041","author":"Lawonn","year":"2019","journal-title":"Proceedings of the Eleventh International Conference on Machine Vision (ICMV 2018)"},{"key":"ref_90","first-page":"5","article-title":"An artificial intelligent methodology-based Bayesian belief networks constructing for big data economic indicators prediction","volume":"14","author":"Mora","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1109\/TAFE.2025.3583334","article-title":"Efficient attention\u2013lightweight deep learning architecture integration for plant pest recognition","volume":"3","author":"Janarthan","year":"2025","journal-title":"IEEE Trans. AgriFood Electron."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"6011","DOI":"10.1007\/s00371-024-03768-7","article-title":"FANN: A novel frame attention neural network for student engagement recognition in facial video","volume":"41","author":"Wang","year":"2025","journal-title":"Vis. Comput."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/10\/669\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T04:37:52Z","timestamp":1761194272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/10\/669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":92,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["a18100669"],"URL":"https:\/\/doi.org\/10.3390\/a18100669","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}