Multi-Face Recognition Based on Convolutional Neural Networks
Keywords:
Facial recognition OpenCV convolutional neural networkAbstract
With the continuous advancement of technology, facial recognition is being applied more and more widely in this era of big data. Techniques based on deep-learning convolutional neural networks are increasingly recognized and adopted. Training facial recognition with convolutional neural networks does not require complex feature extraction; it only needs to use the OpenCV library to detect faces and then employ a suitable network model for automatic training to achieve good recognition performance.
References
Ding, C.; Wu, C. Self-Supervised Learning for Biomedical Signal Processing: A Systematic Review on ECG and PPG Signals. medRxiv 2024.
D. Restrepo, C. Wu, S.A. Cajas, L.F. Nakayama, L.A. Celi, D.M. López. Multimodal deep learning for low-resource settings: A vector embedding alignment approach for healthcare applications. (2024), 10.1101/2024.06.03.24308401
Xie, Minhui, and Shujian Chen. "InVis: Interactive Neural Visualization System for Human-Centered Data Interpretation." Authorea Preprints (2025).
Zhu, Bingxin. "RAID: Reliability Automation through Intelligent Detection in Large-Scale Ad Systems." (2025).
Zhang, Yuhan. "InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment." (2025).
Hu, Xiao. "GenPlayAds: Procedural Playable 3D Ad Creation via Generative Model." (2025).
Qin, Haoshen, et al. "Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression." Digital health 11 (2025): 20552076251349719.
Weng, Yijie, et al. "SafeGen-X: A Comprehensive Framework for Enhancing Security, Compliance, and Robustness in Large Language Models." 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025.
Zhao, Shihao, et al. "KET-GPT: A Modular Framework for Precision Knowledge Updates in Pretrained Language Models." 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025.
Li, Xuan, et al. "MLIF-Net: Multimodal Fusion of Vision Transformers and Large Language Models for AI Image Detection." 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025.
Chen, Rensi. "The application of data mining in data analysis." International Conference on Mathematics, Modeling, and Computer Science (MMCS2022). Vol. 12625. SPIE, 2023.
Chen, Yinda, et al. "Bimcv-r: A landmark dataset for 3d ct text-image retrieval." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024.
Sun, N., Yu, Z., Jiang, N., & Wang, Y. (2025). Construction of Automated Machine Learning (AutoML) Framework Based on Large LanguageModels.
Pal, P. et al. 2025. AI-Based Credit Risk Assessment and Intelligent Matching Mechanism in Supply Chain Finance. Journal of Theory and Practice in Economics and Management. 2, 3 (May 2025), 1–9.
Koutrintzes, D. , Spyrou, E. , Mathe, E. , & Mylonas, P. . (2022). A multimodal fusion approach for human activity recognition. International journal of neural systems, 2350002.
Plexousakis, P. D. . (2005). Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Applications of Artificial Intelligence.
Schwegler, E. , & Challacombe, M. . (1996). Linear scaling computation of the hartree–fock exchange matrix. Journal of Chemical Physics, 105(7), 2726-2734.
Brink, P. J. V. D. , Wijngaarden, R. P. A. V. , Lucassen, W. G. H. , Brock, T. C. M. , & Leeuwangh, P. . (1996). Effects of the insecticide durban 4 e (a.i. chlorpyrifos) in outdoor experimental ditches: ii. community responses and recovery. Environmental Toxicology & Chemistry, 15(7), 1143-1153.
Karp, P. D. , & Paley, S. . (1996). Integrated access to metabolic and genomic data. Journal of Computational Biology, 3(1), 191-212.
Theologos, K. N. , Nikou, I. D. , Lygeros, A. I. , & Markatos, N. C. . (1997). Simulation and design of fluid-catalytic cracking riser-type reactors. AIChE Journal, 43(2), 486-494.
Brazier, F. M. T. , Jonker, C. M. , & Treur, J. . (1996). Formalization of a cooperation model based on joint intentions. Springer, Berlin, Heidelberg.
Martin, W. A. , Church, K. W. , & Patil, R. S. . (1987). Preliminary analysis of a breadth-first parsing algorithm: theoretical and experimental results. Natural Language Parsing Systems, 267-328.
Feng, Y. , & Mizrach, B. . Estimation of Value-at-Risk and Expected Shortfall based on Nonlinear Models of Return Dynamics and Extreme Value Theory.
Xing, Q. , Wang, D. , Huang, F. , & Deng, J. . (2006). Two-dimensional theoretical analysis of the dominant frequency in the inward-emitting coaxial vircator. IEEE Transactions on Plasma Science, 34(3), 584-589.
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