An Efficient Face Recognition Method Using CNN

Convolutional Neural Networks (CNNs) have shown a great success within the field of face recognition. In this paper, we propose a robust face recognition method, which is based on Principal Component Analysis (PCA) and CNN. In our method, PCA is employed to reduce the size of data. Afterwards, we us...

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Bibliographic Details
Published in2021 International Conference of Women in Data Science at Taif University (WiDSTaif ) pp. 1 - 5
Main Authors Fredj, Hana ben, Sghaier, Souhir, Souani, Chokri
Format Conference Proceeding
LanguageEnglish
Published IEEE 30.03.2021
Subjects
CNN
Data processing
Data science
Deep learning
Extraction features
Face recognition
Graphics processing units
Memory management
Regulation
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Summary:Convolutional Neural Networks (CNNs) have shown a great success within the field of face recognition. In this paper, we propose a robust face recognition method, which is based on Principal Component Analysis (PCA) and CNN. In our method, PCA is employed to reduce the size of data. Afterwards, we use a CNN as a classifier for face recognition. We relatively decrease the number of layers used in the CNN architecture, utilizing the dropout regulation technique. Most importantly, we implement the classification step on the GPU component. Several experiments have been implemented using available well-known databases. The experimental results verify the effectiveness of our approach, which keeps good recognition accuracy. Thus, it expands an important acceleration of the classification face compared to the standard CNN implementation without data reduction. It achieves also lower memory consumption due to the smaller amounts of data processing used through the PCA method. Moreover, our model is intentionally designed such that both its running time and memory requirements are decreased.
DOI:10.1109/WiDSTaif52235.2021.9430209