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Recent Advances in Computer Science and Communications

Author(s): Ratnesh Kumar Shukla*, Arvind Kumar Tiwari and Ashish Ranjan Mishra

DOI: 10.2174/0126662558282684240213062932

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Face Recognition Using LBPH and CNN

Article ID: e150324228026 Pages: 11

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Abstract

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security; however, the most difficult challenge is to accurately recognise faces without creating any false identities.

Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms.

Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN.

Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result.

Keywords: Biometric equipment, convolution neural network, face recognition model, local binary pattern, LBP histogram, artificial intelligence.

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