Convolutional neural network transfer learning for robust face recognition in NAO humanoid robot

<abstract> <sec><title>Purpose</title>Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). </sec> <sec><title>Methods</title>In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. </sec> <sec><title>Results</title>The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. </sec> <sec><title>Conclusions</title>This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency. </sec> </abstract>

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Potential of Robust Face Recognition from Real-Time CCTV Video Stream for Biometric Attendance Using Convolutional Neural Network

Advances in Intelligent Systems and Computing - Intelligent Data Engineering and Analytics ◽

10.1007/978-981-15-5679-1_2 ◽

2020 ◽

pp. 11-20

Author(s):

Suresh Limkar ◽

Shashank Hunashimarad ◽

Prajwal Chinchmalatpure ◽

Ankit Baj ◽

Rupali Patil

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Real Time ◽

Video Stream ◽

Robust Face Recognition

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Face recognition based on full convolutional neural network based on transfer learning model

Computer Science and Information Systems ◽

10.2298/csis200922028f ◽

2021 ◽

pp. 28-28

Author(s):

Zhongkui Fan ◽

Ye-Peng Guan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Face Recognition ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Test Accuracy ◽

Great Success ◽

Data Set ◽

Face Features ◽

The Face

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

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Deep convolutional neural network with new training method and transfer learning for structural fault classification of vehicle instrument panel structure

Journal of Mechanical Science and Technology ◽

10.1007/s12206-020-1009-3 ◽

2020 ◽

Vol 34 (11) ◽

pp. 4489-4498

Author(s):

Sang-Yun Lee ◽

Sang-Kwon Lee

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Deep Convolutional Neural Network ◽

Fault Classification ◽

Instrument Panel ◽

Training Method

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Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

Scientific Reports ◽

10.1038/s41598-020-78129-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Young-Gon Kim ◽

Sungchul Kim ◽

Cristina Eunbee Cho ◽

In Hye Song ◽

Hee Jin Lee ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Frozen Section ◽

Medical Center ◽

External Validation ◽

Model Performance ◽

Classification Model ◽

Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

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