Face recognition based on convolutional neural network

doi:10.1109/MEES.2017.8248937

Paper

Face recognition based on convolutional neural network

Published Nov 1, 2017 · Musab Coskun, A. Uçar, Özal Yildirim

2017 International Conference on Modern Electrical and Energy Systems (MEES)

UNKNOWN SJR score

221

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5

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Abstract

Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.

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Study Snapshot

Key takeawayThe modified Convolutional Neural Network (CNN) architecture with batch normalization improves face recognition accuracy and speed in real-world applications like video surveillance and security systems.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Face recognition based on convolutional neural network

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