Design and Application of Face Recognition Algorithm Based on Improved Backpropagation Neural Network

Huang, Qiao; Cui, Limin

doi:10.18280/ria.330105

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…If in the testing process, the image to be tested is an image using glasses, then one of the images using glasses must be entered into the training image database. As in the case with face expressions, and the level of face tilt, there must be an image of a face with several face expressions and the level of face tilt entered in the training image database [7].…”

Section: Discussionmentioning

confidence: 99%

“…In previous research, the success rate of face image recognition using the Backpropagation method was 98% [6]. Also, other research concluded that the Backpropagation Method with improvised Scaled Conjugate Gradient (SCG) generates average recognition rate of 93% and an average number of iterations of 815 [7]. The KSOM method was chosen because it is one of ANN the most widely used unsupervised learning methods, with a high accuracy value, and a short recognition time process.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition

Moonlight

Faizah

Suprapto

et al. 2021

JISEBI

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Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition

Moonlight

Faizah

Suprapto

et al. 2021

JISEBI

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“…In gait recognition, image sequence is the basis of most research methods [11]. The image sequence-based methods generally collect gait information over long distance by camera, and identifies the gait through feature extraction and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition and Difference Analysis of Human Walking Gaits Based on Intelligent Processing of Video Images

Wang¹,

Yang²,

Li³

2020

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Based on the residual network and long short-term memory (LSTM) network, this paper proposes a human walking gait recognition method, which relies on the vector image of human walking features and the dynamic lower limb model with multiple degrees-of-freedom (DOFs). Firstly, a human pose estimation algorithm was designed based on deep convolutional neural network (DCNN), and used to obtain the vector image of human walking features. Then, the movements of human lower limbs were described by a simplified model, and the dynamic eigenvectors of the simplified model were obtained by Lagrange method, revealing the mapping relationship between eigenvectors in gait fitting. To analyze the difference of human walking gaits more accurately, a feature learning and recognition algorithm was developed based on residual network, and proved accurate and robust through experiments on the data collected from a public gait database.

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“…The artificial neural network trained using a back propagation (BP) algorithm is most popular and widely used [18]. The evaluation of the artificial neural network is divided into two stages: the first one is feed forward stage and second one is back propagation stage.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Application and Comparison of Machine Learning Algorithms for Predicting Mass Loss of Cement Raw Materials due to Decarbonation Process

Boukhari¹

2020

RIA

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The purpose of this work is to predict the mass loss of cement raw materials during the decarbonation process. The mass loss is influenced by the interaction of several parameters such as chemical composition of raw material, particle size, temperature range of decarbonation and time exposed. Therefore, predicting mass loss based on experimental parameters data is often challenging. For this reason, various machine learning algorithms such as deep networks using autoencoder DN-AE, artificial neural networks optimized by particle swarm optimization PSO-ANN, ANN optimized by ant colony optimization ACO-ANN and ANN are proposed to predict the mass loss. In this research, all models have been applied successfully to predict the mass loss with high accuracy. The results obtained have shown the superiority of DN-AE compared to PSO-ANN, ACO-ANN and ANN. In addition, PSO-ANN and ACO-ANN have a better performance than the individual use of ANN. The values of adjusted R2 indicate that 99.11%, 98.66%, 98.27% and 97.03% of data are explained by DN-AE, ACO-ANN, PSO-ANN and ANN respectively with scatter index (SI) less than 0.1 and maximum error less than 3.32%. Finally, the results justify that all models proposed can be employed to predict the mass loss as alternative tools.

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Design and Application of Face Recognition Algorithm Based on Improved Backpropagation Neural Network

Cited by 10 publications

References 4 publications

Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition

Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition

Recognition and Difference Analysis of Human Walking Gaits Based on Intelligent Processing of Video Images

Application and Comparison of Machine Learning Algorithms for Predicting Mass Loss of Cement Raw Materials due to Decarbonation Process

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