AI-Based Call Center Management
Main Article Content
Abstract
Call centers today operate within complex ecosystems where surveillance technology, digitalization, and process automation are pivotal. These advancements enable multi-channel communication, personalized service, and proactive customer support. Unlike traditional models centered solely on phone interactions, modern call centers leverage digital tools to enhance operational efficiency. A significant innovation lies in the application of image processing techniques, including face recognition algorithms. These technologies automate tasks, minimizing human intervention and optimizing workflow. In this context, a proposed artificial intelligence-driven call center management system aims to replicate office environments remotely. It focuses on ensuring high service quality and security through real-time monitoring of representatives. Key features include facial recognition accuracy rates of 99% for detection and 96.88% for recognition. This system distinguishes live faces from photographs using cascade location detection, a novel approach that enhances fraud prevention compared to current methods. Integrating such advanced technologies into call centers marks a transformative step towards efficient, secure, and personalized customer service experiences in the digital age. Only the video call recordings are utilized for all analyses without additional equipment or data sources. Therefore, this easily implementable management system is introduced at a minimal cost.Top of Form
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Örmeci, E. L., Salman, F. S., & Yücel, E. (2014). Staff rostering in call centers providing employee transportation. Omega, 43, 41-53. DOI: https://doi.org/10.1016/j.omega.2013.06.003
Phung-Duc, T., & Kawanishi, K. I. (2014). Performance analysis of call centers with abandonment, retrial and after-call work. Performance Evaluation, 80, 43-62. DOI: https://doi.org/10.1016/j.peva.2014.03.001
Xia, C. H., & Dube, P. (2007). Dynamic pricing in e‐services under demand uncertainty. Production and Operations Management, 16(6), 701-712. DOI: https://doi.org/10.1111/j.1937-5956.2007.tb00290.x
Christl, W. (2023). Surveillance and algorithmic control in the call centre. A case study on contact and service center software, automated management and outsourced work. Cracked Labs.
Yaswanthram, P., & Sabarish, B. A. (2022, January). Face recognition using machine learning models-comparative analysis and impact of dimensionality reduction. In 2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/ICAECC54045.2022.9716590
Farfade, S. S., Saberian, M. J., & Li, L. J. (2015, June). Multi-view face detection using deep convolutional neural networks. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (pp. 643-650). DOI: https://doi.org/10.1145/2671188.2749408
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4690-4699). DOI: https://doi.org/10.1109/CVPR.2019.00482
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017). Sphereface: Deep hypersphere embedding for face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 212-220). DOI: https://doi.org/10.1109/CVPR.2017.713
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823). DOI: https://doi.org/10.1109/CVPR.2015.7298682
Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). Ieee.
Wu, B., Ai, H., Huang, C., & Lao, S. (2004, May). Fast rotation invariant multi-view face detection based on real adaboost. In Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings. (pp. 79-84). IEEE.
Mathias, M., Benenson, R., Pedersoli, M., & Van Gool, L. (2014). Face detection without bells and whistles. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13 (pp. 720-735). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-10593-2_47
Jones, M., & Viola, P. (2003). Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96, 3(14), 2.
Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008, June). A discriminatively trained, multiscale, deformable part model. In 2008 IEEE conference on computer vision and pattern recognition (pp. 1-8). Ieee. DOI: https://doi.org/10.1109/CVPR.2008.4587597
Hangaragi, S., Singh, T., & Neelima, N. (2023). Face detection and Recognition using Face Mesh and deep neural network. Procedia Computer Science, 218, 741-749. DOI: https://doi.org/10.1016/j.procs.2023.01.054
Hu, K., Allon, G., & Bassamboo, A. (2022). Understanding customer retrials in call centers: Preferences for service quality and service speed. Manufacturing & service operations management, 24(2), 1002-1020. DOI: https://doi.org/10.1287/msom.2021.0976
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. 770-778).
Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). Ieee. DOI: https://doi.org/10.1109/CVPR.2001.990517
Xu, W., Fu, Y. L., & Zhu, D. (2023). ResNet and its application to medical image processing: Research progress and challenges. Computer Methods and Programs in Biomedicine, 240, 107660. DOI: https://doi.org/10.1016/j.cmpb.2023.107660
Putra, Y. C., & Wijayanto, A. W. (2023). Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning. Remote Sensing Applications: Society and Environment, 29, 100914. DOI: https://doi.org/10.1016/j.rsase.2022.100914
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. 770-778). DOI: https://doi.org/10.1109/CVPR.2016.90
AT&T Laboratories Cambridge, “The Olivetti faces dataset,” scikit learn. [Online]. Available: https://scikit-learn.org/0.19/datasets/olivetti_faces.html. [Accessed: Feb. 05.2024]
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708). DOI: https://doi.org/10.1109/CVPR.2017.243