Face Recognition Based on Convolutional Neural Networks

doi:10.54097/hset.v16i.2225

Paper

Face Recognition Based on Convolutional Neural Networks

Published Nov 10, 2022 · Rui Liu

Highlights in Science, Engineering and Technology

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Abstract

Since science and technology have been progressing steadily in recent years, deep learning's potential applications have expanded greatly. From unlocking the screen of a phone with a human face to driverless technology, which has emerged in recent years. Facial recognition is proving to be a boon to life. Among various deep learning algorithms, the appearance of convolutional neural network (CNN)has made unprecedented progress in image recognition. In this paper, the basic principles of convolutional neural networks are explained, and the most important concepts are introduced. The convolutional neural network is used for experiments. The input layer, convolution layer, pooling layer, fully connected layer, and output layer are the nine layers that make up the traditional and complete convolutional neural network model, which is used as the experimental foundation. LFW dataset is used for training, and the experimental results are given. At the end of the paper, the accuracy and loss functions are analyzed and the accurate results of facial recognition are achieved.

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Key takeawayConvolutional neural networks (CNNs) have made significant progress in facial recognition, achieving accurate results with the LFW dataset.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Face Recognition Based on Convolutional Neural Networks

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References

A Review of Deep Learning Research

Deep learning techniques have been widely applied in various fields, with common models, optimization methods, and open source frameworks, and future research directions are also discussed.

Face recognition based on convolutional neural network

The 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.

Deep Residual Learning for Image Recognition

Deep residual learning framework enables easier training of deep neural networks, resulting in improved accuracy and performance in image recognition tasks.

Deep Learning

Deep learning techniques have revolutionized machine-learning by discovering intricate structures in high-dimensional data, advancing image recognition, speech recognition, and natural language understanding.

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