Adaptive Histogram Normalization based Loss Function in Deep Learning Algorithm for Face Recognition

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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A Pragmatic Approach to Face Recognition using a Novel Deep Learning Algorithm

2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) ◽

10.1109/icacite51222.2021.9404697 ◽

2021 ◽

Author(s):

Ayush kumar ◽

Arvind Kumar ◽

Puja Kumari ◽

N. Suresh kumar

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Learning Algorithm ◽

Pragmatic Approach ◽

Deep Learning Algorithm

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Based on DICOM RT Structure and Multiple Loss Function Deep Learning Algorithm in Organ Segmentation of Head and Neck Image

IFMBE Proceedings - Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices ◽

10.1007/978-3-030-30636-6_58 ◽

2019 ◽

pp. 428-435

Author(s):

Ya-Ju Hsieh ◽

Hsien-Chun Tseng ◽

Chiun-Li Chin ◽

Yu-Hsiang Shao ◽

Ting-Yu Tsai

Keyword(s):

Deep Learning ◽

Head And Neck ◽

Loss Function ◽

Learning Algorithm ◽

Deep Learning Algorithm ◽

Dicom Rt ◽

Multiple Loss

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Recognition of Masked Face Using Convolutional Neural Network Algorithm

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37051 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 3233-3237

Author(s):

Sai Kiruthika K. M

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Skin Color ◽

Learning Algorithm ◽

Huge Number ◽

Skin Texture ◽

Deep Learning Algorithm ◽

Face Region ◽

Security Problem ◽

Neural Network Algorithm

The covid -19 is an unparalleled crisis resulting in huge number of casualties security problem. So has to scale back the spread of corona virus, people often wear a mask to guard themselves. Indeed, during this challenging context, the matter of face recognition is usually like periocular recognition involving iris, pupil, sclera, upper and lower eyelids, eye folds, eye corners, skin texture, fine wrinkles, complexion, skin color, skin pores etc. In this paper, we propose a reliable method supported discard masked region and deep learning based features so as to deal with the matter of masked face recognition process. The primary step to discard the masked face region. Next, we apply deep learning algorithm to extract the simplest features from obtained regions (mostly eyes and forehead regions). This leads to good accuracy than the previous work for detecting the masked face.

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