Face Detection for Low-Light Face in Real-Time Video using VamStack Platform

Khoeun, Ratanak; Rasmequan, Suwanna; Intaramanee, Tanin; Sungkhun, Sukonthee

doi:10.1109/incit50588.2020.9310971

Cited by 2 publications

(1 citation statement)

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“…We did a thorough evaluation of the literature on in order to contextualize our work and identify research gaps in the fields of Computer vision and image processing. RatanakKhoeun and SuwannaRasmequan [1] described the paper as Face recognition is a popular research topic in computer vision with applications in surveillance and authentication. Face detection is a necessary step for face recognition, but it can be challenging in low-resolution videos.…”

Section: Literature Reviewmentioning

confidence: 99%

Dusky Detect: Facial Detection in Low Light Using CNN

Kumar¹,

Parvathi²,

Manasa³

et al. 2023

Int. J. Res. Publ. Rev.

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At present, Identification of faces through image processing is a crucial task in the fields of Machine Learning and Artificial intelligence. Luminescence plays a key role in the face detection. Nowadays, detecting the faces of different people present in different lightings and different times of day has become strenuous.Dusky face is sticky to predict using traditional methods. In order to find a solution, this project proposes various classifiers and Convolutional Neural Networks to detect the face in Abnormal lighting situations. This idea provides the process and technique of the face detection in low light using CNN. The proposed idea in this project will improve the accuracy and facial recognition in different light intensities using the combinations of various facial recognition techniques and classifiers and also the CNN for obtaining a better model with higher performance metrics.

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Section: Literature Reviewmentioning

confidence: 99%