A method combining multiple perspectives to enhance accuracy in facial recognition problems
128 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.95.2024.76-84Keywords:
Facial recognition; Deep learning; Convolutional neural networks; Integrating multiple perspectives; Image processing; Viewpoint optimization; Multi-perspective analysis; Recognition performance improvement; Security applications.Abstract
This article introduces an advanced method in the field of facial recognition, using a unique technique that combines Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) to integrate different perspectives. The highlight of this method is the application of CNN to analyze image features from multiple angles, along with MLP, to optimize the information synthesis process, thereby enhancing the accuracy of facial recognition under varying lighting conditions and angles. The main goal is to address the challenge of performance degradation in facial recognition in real-world situations, especially when there is a significant change in the viewpoint. This study details the model-building process from data collection and processing, training complex neural networks, and evaluating effectiveness through standard and experimental datasets.
References
[1]. Zhao W., Krishnaswamy A., Chellappa R., Swets D.L., Weng J. “Discriminant Analysis of Principal Components for Face Recognition”. In: Wechsler H., Phillips P.J., Bruce V., Soulié F.F., Huang T.S. (eds) Face Recognition. NATO ASI Series, vol 163. Springer, (1998). DOI: https://doi.org/10.1007/978-3-642-72201-1_4
[2]. Guodong Guo, Stan Z. Li, Kap Luk Chan., “Support Vector machines for face recognition”, School of Electrical and Electronic, Nanyang Technology University, (2001).
[3]. Liao, S., Jain, A. K., Li, S. Z., “Partial face recognition: Alignment-free approach”, IEEE Trans. PAMI, 35(5):1193–1205, (2013). DOI: https://doi.org/10.1109/TPAMI.2012.191
[4]. Y. Taigman, M. Yang, M. Ranzato, L. Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification”, Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2014), Columbus, (2014).
[5]. Y. Taigman, M. Yang, M. Ranzato and L. Wolf, "DeepFace: Closing the Gap to Human-Level Performance in Face Verification," IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, (2014). DOI: https://doi.org/10.1109/CVPR.2014.220
[6]. A. Verma, D. Malla, A. K. Choudhary and V. Arora, "A Detailed Study of Azure Platform & Its Cognitive Services" International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, (2019). DOI: https://doi.org/10.1109/COMITCon.2019.8862178
[7]. V. Sharma, "Object Detection and Recognition using Amazon Rekognition with Boto3", 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, (2022). DOI: https://doi.org/10.1109/ICOEI53556.2022.9776884
[8]. Baojin Huang, Zhongyuan Wang, Guangcheng Wang, Kui Jiang, Zhen Han, Tao Lu, Chao Liang, " PLFace: Progressive Learning for Face Recognition with Mask Bias" College of Information Engineering, Northwest A&F University, (2023). DOI: https://doi.org/10.1016/j.patcog.2022.109142
[9]. Xiaopeng Li, Yuyun Xiang, Shuqin Li, "Combining convolutional and vision transformer structures for sheep face recognition " Computers and Electronics in Agriculture, vol. 205, p. 107651, (2023). DOI: https://doi.org/10.1016/j.compag.2023.107651
[10]. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection" IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2005).
