Human Face Detection under Complex Lighting Conditions

Moazzam, Golam; Parveen, R; Bhuiyan, Md. Al-Amin

doi:10.14569/specialissue.2011.010112

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Owing to the problems of using thermal, NIR, and SWIR cameras, face detection studies using visible-light cameras, which are inexpensive and require no additional equipment, have been actively conducted [ 23 , 24 , 25 , 26 ]. The authors in [ 23 , 24 ] studied face detection using a skin segmentation method in various illumination environments.…”

Section: Related Workmentioning

confidence: 99%

“…Li et al [ 25 ] conducted nighttime real-time face detection research using the promotion normalized pixel difference (PRO-NPD) feature [ 16 ], which presents the ratio of the difference to the sum between two pixel values. Moazzam et al [ 26 ] detected faces using the genetic algorithm (GA) [ 28 ] in complex lighting conditions. This method, however, has a disadvantage in that the preprocessing is complicated and only one face is found among multiple faces in the image.…”

Section: Related Workmentioning

confidence: 99%

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Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Cho

Baek

Kim

et al. 2018

Sensors

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Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to detect faces directly from the entire image due to noise and image blur. Therefore, we propose Two-Step Faster region-based convolutional neural network (R-CNN) based on the image preprocessed by histogram equalization (HE). As a two-step scheme, our method sequentially performs the detectors of body and face areas, and locates the face inside a limited body area. By using our two-step method, the processing time by Faster R-CNN can be reduced while maintaining the accuracy of face detection by Faster R-CNN. Using a self-constructed database called Dongguk Nighttime Face Detection database (DNFD-DB1) and an open database of Fudan University, we proved that the proposed method performs better compared to other existing face detectors. In addition, the proposed Two-Step Faster R-CNN outperformed single Faster R-CNN and our method with HE showed higher accuracies than those without our preprocessing in nighttime face detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Cho

Baek

Kim

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Also, mutation was used with particle swarm optimization technique [27], whose role is to optimize the difficult problems of mathematics in continuous or binary space [28]. Lastly, the importance of mutation operator is emphasized in practical applications of genetic algorithms, like the ones regarding the alignment of multiple molecular sequences [29], the detecting of human faces in an image with complex background [30] and the modeling of neural image compression [31].…”

Section: Related Workmentioning

confidence: 99%

Translation of the Mutation Operator from Genetic Algorithms to Evolutionary Ontologies

Contras¹,

Matei²

2016

ijacsa

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Abstract-Recently introduced, evolutionary ontologies represent a new concept as a combination of genetic algorithms and ontologies. We have defined a new framework comprising a set of parameters required for any evolutionary algorithm, i.e. ontological space, representation of individuals, the main genetic operators such as selection, crossover, and mutation. Although a secondary operator, mutation proves its importance in creating and maintaining evolutionary ontologies diversity. Therefore, in this article, we widely debate the mutation topic in evolutionary ontologies, marking its usefulness in practice by experimental results. Also we introduce a new mutation operator, called relational mutation, concerning mutation of a relationship through its inverse.

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Human Face Detection under Complex Lighting Conditions

Cited by 2 publications

References 9 publications

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Translation of the Mutation Operator from Genetic Algorithms to Evolutionary Ontologies

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