Convolutional Neural Network based face detection

Mukherjee, Subham; Saha, Sumalya; Lahiri, Sounak; Das, Ayan; Bhunia, Ayan Kumar; Konwer, Aishik; Chakraborty, Arindam

doi:10.1109/iementech.2017.8076987

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…A comparison is performed in a tabular manner in this paper, so that it is easier to understand the algorithms in a better and easier manner. S. Mukherjee et al [6], in this paper discusses about two different techniques that are used for face detection in frames which contain humans. The old approach of using hand crafted features followed by training a simple classifier and a relatively new approach of learning features form data with the help of neural network.…”

Section: Literature Surveymentioning

confidence: 99%

Social Distance Detection

Abraham¹,

Aathmaram²,

D³

et al. 2022

IJEAST

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COVID-19 is an illness caused by the SARSCoV-2 virus. The majority of COVID-19 patients will have low to normal symptoms and will recover without requiring extra care. Otherwise, some patients will become extremely sick and require medical support. A Deep Learning model is made to track people and measure the distance between them. To prevent the transmission of the infection, making people maintain the minimum distance is the key objective. We apply YOLOv3 and other convolutional neural network-based methods, as well as a distance measurement method, to recognise individuals. This Deep Learning model can determine whether members of a large group maintain their distance from one another. If they maintain a 6 feet distance, it will be indicated in green; if not, it will be highlighted in red. This can help identify social disparities and lessen the spread of COVID-19

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Section: Literature Surveymentioning

confidence: 99%

Social Distance Detection

Abraham¹,

Aathmaram²,

D³

et al. 2022

IJEAST

View full text Add to dashboard Cite

show abstract

“…Recently, convolutional neural networks (CNNs) achieve remarkable progresses in a variety of computer vision tasks [25,26,27]. Taking image classification [17,23] and face recognition [18] [29] have discussed the formulation for both the methods, i.e., using hand-crafted features followed by training a simple classifier and an entirely modern approach of learning features from data using neural networks. Ren et al [30] have presented a method for real time detection and tracking of the human face.…”

Section: Face Detectionmentioning

confidence: 99%

“…Mukherjee et al. [29] have discussed the formulation for both the methods, i.e., using hand‐crafted features followed by training a simple classifier and an entirely modern approach of learning features from data using neural networks. Ren et al.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous face detection based on multi‐task cascaded convolutional neural network

Yang

Zhang

2021

IET Image Processing

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Facial target detection is an important task in computer vision. Because heterogeneous face detection shows broad prospects, it has attracted extensive attention from the academic community. In recent years, with the rise of deep learning and its applications in computer vision, face detection technology has made great strides. This paper uses multi-task cascaded convolutional neural network (MTCNN) for heterogeneous face feature detection. This algorithm makes full use of the advantages of image pyramid, boundary regression, fully convolutional attention networks and non-maximum suppression. The main idea of this paper is to use candidate frame plus classifier for fast and efficient face detection. Specifically, the candidate window is generated by the proposal network (P-Net), and the high-precision candidate window is filtered and selected by the reduced network (R-Net), and the final bounding box and facial key points are generated by the output network (O-Net). In order to prove the effectiveness of this method in visible light, near-infrared and sketch face recognition scenes, it was verified in the datasets of CUFS, CUFSF and CASIA NIR-VIS 2.0. Experiments show that this method is effective for face images in heterogeneous face and is better than the latest algorithms.

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“…However, methods that can be applied to URTA are very limited, especially under a plasma process. Conventionally, contact-type methods, such as thermocouples 2,3) and thermo-labels, 4) need to come into contact with the wafer and are typically pasted to either the backside of the wafer or the wafer chuck. Thus, contact thermal resistance between the wafer and the probe will inevitably affect measurements.…”

Section: Introductionmentioning

confidence: 99%

Development of a real-time temperature measurement technique for SiC wafer during ultra-rapid thermal annealing based on optical-interference contactless thermometry (OICT)

Hanafusa

Higashi

2023

Jpn. J. Appl. Phys.

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Real-time temperature measurement technique with high spatial (≤20 μm) and temporal (≤1 μs) resolutions for SiC wafer during ultra-rapid thermal annealing (URTA) has been developed based on optical-interference contactless thermometry (OICT). This technique consists of hardware (OICT imaging setup) and software (fast temperature extraction program). Under URTA by atmospheric-pressure thermal plasma jet (TPJ), clear variation of optical interference fringes was observed by a high-speed camera (HSC) and then analyzed by a fast temperature extraction program using image preprocessing and a database. 3.5-dimensional (x, y, z and time) temperature distribution in SiC wafer was obtained within 0.43 second.

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Convolutional Neural Network based face detection

Cited by 7 publications

References 5 publications

Social Distance Detection

Social Distance Detection

Heterogeneous face detection based on multi‐task cascaded convolutional neural network

Development of a real-time temperature measurement technique for SiC wafer during ultra-rapid thermal annealing based on optical-interference contactless thermometry (OICT)

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