Face Recognition Using ELM with ResNet50

DOI

10.2991/978-94-6463-529-4_13How to use a DOI?

Keywords

Face recognition; Extreme Learning Machine; ResNet50; Feature extraction; Classification

Abstract

Facial recognition is a complex problem that has received a great deal of attention due to its numerous applications, including security, surveillance, and identification. In this study, we provide a novel method that combines the strength of the Extreme Learning Machine (ELM) algorithm with the ResNet50 deep neural network for accurate and efficient face recognition. The two primary steps in our method are feature extraction and categorization. In the first step, we use the ResNet50 network to extract complex features from facial photos. An ELM classifier is then fed these features, which is a fast and efficient learning algorithm that is particularly suited for high-dimensional data. To determine whether our strategy is effective, we conducted experiments on a popular face recognition dataset, namely, AT&T Dataset on Faces. According to the results of our experiments, our suggested strategy is more accurate and effective than a number of cutting-edge approaches. Our findings demonstrate that the combination of ResNet50 and ELM gives a strong and effective response to the face recognition issue. This approach has significant potential for real-world applications where high accuracy and speed are crucial.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Robins Anand
AU  - Tripti Goel
PY  - 2024
DA  - 2024/10/04
TI  - Face Recognition Using ELM with ResNet50
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 130
EP  - 141
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_13
DO  - 10.2991/978-94-6463-529-4_13
ID  - Anand2024
ER  -