Facial Recognition Model Using Custom Designed Deep Learning Architecture

Title

Facial Recognition Model Using Custom Designed Deep Learning Architecture

Creator

Kesarwani T.; Mittal R.; Panwar D.; Saini G.L.; Kumar S.

Description

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), Nae 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. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-969 LNNS, pp. 457-467.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial intelligence; Deep learning; Facial recognition

Coverage

Kesarwani T., Manipal University Jaipur, Rajasthan, Jaipur, India; Mittal R., Manipal University Jaipur, Rajasthan, Jaipur, India; Panwar D., Manipal University Jaipur, Rajasthan, Jaipur, India; Saini G.L., Manipal University Jaipur, Rajasthan, Jaipur, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bangalore, 560074, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981972081-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-2082-8_32

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196768721&doi=10.1007%2f978-981-97-2082-8_32&partnerID=40&md5=f6468aca2182f0ca9a1d5ddf570b2c36