A Face Recognition Algorithm Based on Angular Distance Loss Function and Convolutional Neural Network

<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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Face Detection and Recognition Method Based on Improved Convolutional Neural Network

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2021.15.85 ◽

2021 ◽

Vol 15 ◽

pp. 774-781

Author(s):

Zhengqiu Lu ◽

Chunliang Zhou ◽

Xuyang Xuyang ◽

Weipeng Zhang

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Rapid Development ◽

Weighted Average ◽

Recognition Algorithm ◽

Equal Treatment ◽

Learning Technology ◽

Main Research ◽

Common Face

with rapid development of deep learning technology, face recognition based on deep convolutional neural network becomes one of the main research methods. In order to solve the problems of information loss and equal treatment of each element in the input feature graph in the traditional pooling method of convolutional neural network, a face recognition algorithm based on convolutional neural network is proposed in this paper. First, MTCNN algorithm is used to detect the faces and do gray processing, and then a local weighted average pooling method based on local concern strategy is designed and a convolutional neural network based on VGG16 to recognize faces is constructed which is finally compared with common convolutional neural network. The experimental results show that this method has good face recognition accuracy in common face databases.

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Face Recognition Algorithm Based On Convolutional Neural Network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1550/2/022023 ◽

2020 ◽

Vol 1550 ◽

pp. 022023

Author(s):

Shiyuan Xu ◽

Shicheng Xu ◽

Zhaojun Wang ◽

Chengyan Pu ◽

Qingchuan Meng

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Recognition Algorithm

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Vision-Based Spacecraft Pose Estimation via a Deep Convolutional Neural Network for Noncooperative Docking Operations

Aerospace ◽

10.3390/aerospace7090126 ◽

2020 ◽

Vol 7 (9) ◽

pp. 126 ◽

Cited By ~ 1

Author(s):

Thaweerath Phisannupawong ◽

Patcharin Kamsing ◽

Peerapong Torteeka ◽

Sittiporn Channumsin ◽

Utane Sawangwit ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Loss Function ◽

Pose Estimation ◽

High Performance ◽

Degrees Of Freedom ◽

Recognition Algorithm ◽

Deep Convolutional Neural Network ◽

Estimation Model ◽

Detection And Tracking

The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.

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