Research on face detection under different lighting

Lee, Hai-Wu; Peng, Fan-Fan; Lee, Xiu-Yun; Dai, Hong-Nian; Zhu, Ying

doi:10.1109/icasi.2018.8394486

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Fan-Fan Peng et al [24] shows a technique that has insuperable defect, particularly when the lightning changes, the detection e↵ect quickly decreases and is not sufficient for the actual system. The motivation behind this study is to take care standard issue of face detection under various lights, and foster a precise and e↵ective human face detection algorithm using Visual Studio 2015 and OpenCV technology.…”

Section: Literature Surveymentioning

confidence: 99%

Supervisory framework for threat detection withmultilayer processing in CNN

Mule¹,

Pandit²,

Karmakar³

et al. 2022

ITM Web Conf.

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Face recognition has received huge acknowledgement due to its various uses in Internet communication, security, access control, surveillance, PC entertainment and law enforcement. Conventional methods of recognition based on the ownerships of identity-cards or full knowledge such as a security number or password are not totally solid. Physical ID cards can be lost or forged, passwords can be hacked or forgotten but a face is undoubtedly connected to its owner. It cannot be stolen, borrowed or easily forged. Our current system has a lot of weaknesses wherever it is simply taken and merged. The focus of this paper is to help users for development of the security by utilizing face identification and recognition. The proposed framework principally comprises of subsystems specifically picture capture, face identification and detection, email alerts and metal detection. Furthermore, the improvement in Computer Vision through Deep Learning algorithms has been an impressive achievement, especially with the Convolutional Neural Network algorithm. A convolutional neural network is a feed-forward neural network that is by and large used to break down visual pictures by using grid-like topology. It is also called as ConvNet. The objects in a picture are distinguished and arranged using convolutional neural network. CNN detects various simple complex patterns in images and data in its different layers of Convolution Layer, Max Polling Layer and Fully Connected Layer .This field intends to allow and configure machines to see the world as people do, and utilize the information for doing tasks (such as Image Analysis, Image Recognition and Classification, etc).

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Section: Literature Surveymentioning

confidence: 99%

Supervisory framework for threat detection withmultilayer processing in CNN

Mule¹,

Pandit²,

Karmakar³

et al. 2022

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…The third technique focused on designing an effective lighting source to compensate for poor lighting conditions. For example, in [8], an imaging definition feedback-based method was developed. Proper use of sensors can obtain images of better quality; however their costs are always high.…”

mentioning

confidence: 99%