Facial Recognition Model Using Custom Designed Deep Learning Architecture

被引:0
|
作者
Kesarwani, Tanish [1 ]
Mittal, Rohit [1 ]
Panwar, Deepak [1 ]
Saini, G. L. [1 ]
Kumar, Sandeep [2 ]
机构
[1] Manipal Univ Jaipur, Jaipur, Rajasthan, India
[2] CHRIST Deemed Univ, Sch Engn & Technol, Dept Comp Sci & Engn, Kengeri Campus, Bangalore 560074, Karnataka, India
来源
COMMUNICATION AND INTELLIGENT SYSTEMS, VOL 3, ICCIS 2023 | 2024年 / 969卷
关键词
Facial recognition; Deep learning; Artificial intelligence; ROBOT;
D O I
10.1007/978-981-97-2082-8_32
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Naive Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix.
引用
收藏
页码:457 / 467
页数:11
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