OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network

Lokku, Gurukumar; Reddy, G. Harinatha; Prasad, M. N. Giri

doi:10.1016/j.asoc.2021.108365

Cited by 26 publications

(13 citation statements)

References 32 publications

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“…Moreover, as the number of hyperparameters and the range of values increase, it becomes quite difficult to manage [21]. To overcome the drawbacks of manual search, automatic search algorithms have been proposed, such as grid search [22][23][24][25]. Mainly, grid search trains machine learning models with different values of hyperparameters in the training set and compares the performance according to evaluation metrics.…”

Section: Hyperparameter Optimization For Machine Learning Modelsmentioning

confidence: 99%

“…Lokku, Reddy, Prasada (2022) [24] Proposed a CNN-based classifier for Optimal Face Recognition Network (OPFaceNet) to recognize facial images affected by high noise and occlusion. This algorithm identifies the hyperparameters for CNN model by optimizing the Fitness Sorted Rider Optimization Algorithm (FS-ROA) and achieves a good recognition rate of 97.2%.…”

Section: Scheme Achievement Limitationsmentioning

confidence: 99%

“…In essence, the network architecture of a TCN is an extension of a 1D CNN in which a series of 1D convolutional layers are stacked on one another. [24] In the experiment, we analysed three different stack numbers n (i.e., 2, 3, 4) from transferring learning cases in the TCN predictor.…”

Section: Model Training Phasementioning

confidence: 99%

“…The architectural components of the proposed TCN model from GitHub [24] were regarded as the initial values and are listed in Table 5. Compute Reward R using Equation ( 14).…”

Section: Model Training Phasementioning

confidence: 99%

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Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

et al. 2022

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Deep learning networks (DLNs) use multilayer neural networks for multiclass classification that exhibit better results in wind-power forecasting applications. However, improving the training process using proper parameter hyperisations and techniques, such as regularisation and Adam-based optimisation, remains a challenge in the design of DLNs for processing time-series data. Moreover, the most appropriate parameter for the DLN model is to solve the wind-power forecasting problem by considering the excess training algorithms, such as the optimiser, activation function, batch size, and dropout. Reinforcement learning (RN) schemes constitute a smart approach to explore the proper initial parameters for the developed DLN model, considering a balance between exploration and exploitation processes. Therefore, the present study focuses on determining the proper hyperparameters for DLN models using a Q-learning scheme for four developed models. To verify the effectiveness of the developed temporal convolution network (TCN) models, experiments with five different sets of initial parameters for the TCN model were determined by the output results of Q-learning computation. The experimental results showed that the TCN accuracy for 168 h wind power prediction reached a mean absolute percentage error of 1.41%. In evaluating the effectiveness of selection of hyperparameters for the proposed model, the performance of four DLN-based prediction models for power forecasting—TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models—were compared. The overall detection accuracy of the TCN model exhibited higher prediction accuracy compared to canonical recurrent networks (i.e., the GRU, LSTM, and RNN models).

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Section: Hyperparameter Optimization For Machine Learning Modelsmentioning

confidence: 99%

Section: Scheme Achievement Limitationsmentioning

confidence: 99%

Section: Model Training Phasementioning

confidence: 99%

“…The architectural components of the proposed TCN model from GitHub [24] were regarded as the initial values and are listed in Table 5. Compute Reward R using Equation ( 14).…”

Section: Model Training Phasementioning

confidence: 99%

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Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

et al. 2022

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“…Initially, in facial recognition research, researchers favored machine learning methods. Initially, facial recognition was only at the 2-dimensional level [6][7][8][9]. Commonly used machine learning methods include principal component analysis and support vector machines.…”

Section: Introductionmentioning

confidence: 99%

3D Face Recognition Neural Network for Digital Human Resource Management

Dong¹

2022

Scientific Programming

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Digital human resource management can improve the organizational and operational efficiency of enterprises.In order to improve the efficiency of enterprise digital management and solve the problems of low security level and insufficient stability of 2D face recognition, we introduce 3D face recognition into the digital human resource management system. We propose a face recognition method based on a multistream convolutional neural network and local binary pattern and build a digital face recognition management system. We first build the system computer vision scene. Then a local binary mode facial expression feature extraction scheme is designed according to the depth camera image extraction method. Considering that face 3D features are easy to be missed, we build a multistream convolutional neural network to learn facial 3D features. Finally, we validate the effectiveness of the method in selecting a public face dataset. Experiments prove that our method can reach 98% face recognition accuracy, which is significantly better than other methods.

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