Title: Research on intelligent access control technology of face recognition model based on parameter sharing and dense connection

Authors: Yonghua Xu

Addresses: School of Computer Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, 211169, China; Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing, 211169, China

Abstract: This study proposes a laboratory intelligent facial recognition system based on improved CNN, which significantly improves the accuracy of facial recognition by optimising the portrait recognition algorithm, improving CNN calculation and large parameter scale, and utilising perspective projection to improve portrait effect and sample utilisation. The experimental results show that the recognition rate has been improved by 10%, the CPU usage rate is less than 100%, and the model parameters have been reduced by more than 95%. This system can effectively and accurately recognise faces, making it suitable for embedded facial recognition devices.

Keywords: convolutional neural network; CNN; face recognition; liveness detection; intelligent access control.

DOI: 10.1504/IJCISTUDIES.2023.137844

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.3/4, pp.255 - 270

Received: 11 Oct 2022
Accepted: 22 Sep 2023
Published online: 05 Apr 2024 *

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