[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9717/kmms.2021.24.5.701

Human Face Recognition Based on improved CNN Model with Multi-layers

Zhang, Ruyang (Dept. of Information & Communication Engineering, Tongmyong University)
Lee, Eung-Joo (Dept. of Information & Communication Engineering, Tongmyong University)

Publication Information

Journal of Korea Multimedia Society / v.24, no.5, 2021 , pp. 701-708 More about this Journal

Abstract

As one of the most widely used technology in the world right now, Face recognition has already received widespread attention by all the researcher and institutes. It has been used in many fields such as safety protection, surveillance system, crime control and even in our ordinary life such as home security and so on. This technology with today's technology has advantages such as high connectivity and real time transformation. But we still need to improve its recognition rate, reaction time and also reduce impact of different environmental status to the whole system. So in this paper we proposed a face recognition system model with improved CNN which combining the characteristics of flat network and residual network, integrated learning, simplify network structure and enhance portability and also improve the recognition accuracy. We also used AR and ORL database to do the experiment and result shows higher recognition rate, efficiency and robustness for different image conditions.

Keywords

Face Recognition; CNN Model; ORL Database; AR Face Database; Residual Network;

Citations & Related Records

Reference

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