Robust Face Detection integrating Novel Skin Color Matching under Variant Illumination Conditions

Akash, Asif Anjum; Akhand, M. A. H.; Siddique, Nazmul

doi:10.5815/ijigsp.2021.02.01

Cited by 4 publications

(4 citation statements)

References 46 publications

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“…In the category of deep learning-based face detection methods, Akash et al (2021) proposed a method based on feature pyramid and triplet loss for training single-level deep neural networks for face detection and recognition. The computational complexity was reduced by sharing the weights, but the performance of a single deep neural network for analyzing small-scale and dense faces was not ideal.…”

Section: Related Workmentioning

confidence: 99%

“…To demonstrate the detection performance of the proposed algorithm, comparative experiments were conducted with Verma et al (2022), Akash et al (2021), Tsai andChi (2022), andHou et al (2021).…”

Section: Comparative Experiments Of Different Algorithmsmentioning

confidence: 99%

“…However, ineffective image feature extraction algorithms resulted in poor detection performance. Akash et al (2021) proposed a skin color matching method with unchanged lighting conditions in the face detection process. The method balanced highintensity and low-intensity facial images through separate rules.…”

Section: Related Workmentioning

confidence: 99%

“…However, their detection performance needs to be improved because neither method considers the occlusion factors. Verma et al (2022) and Akash et al (2021) use traditional detection algorithms. However, their detection performance is poor for small-scale and complex occlusion scenes with mPA50 below 90%.…”

Section: Comparison Of Different Algorithms On the Aizoo Datasetmentioning

confidence: 99%

See 3 more Smart Citations

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

Qi,

Wang,

Dong

2024

International Journal of Gaming and Computer-Mediated Simulations

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This paper proposes an advanced approach for detecting faces with mask occlusion based on YOLOv5 to address various challenges encountered in face detection, including illumination blur and occlusion. The proposed methodology involves the integration of a convolutional block attention module into the backbone network and different network levels in the neck of the YOLOv5s. This approach enables the suppression of irrelevant features and emphasizes the identification of masked facial features. Replacing the conventional loss function with the Focal Loss function addresses the problem of sample imbalance. The enhanced YOLOv5s network was applied to detect mask-occluded faces. Empirical evaluations were conducted on the WIDER Face and AIZOO datasets. The simulation comparison results demonstrate that the proposed method achieves superior real-time detection performance, fulfilling the objective of developing a lightweight detection model.

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Section: Related Workmentioning

confidence: 99%

Section: Comparative Experiments Of Different Algorithmsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison Of Different Algorithms On the Aizoo Datasetmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

Qi,

Wang,

Dong

2024

International Journal of Gaming and Computer-Mediated Simulations

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Face Skin Color Detection Method Based on YUV-KL Transform

Chen

Yuan

2022

2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

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Personality Trait Detection Based on ASM Localization and Deep Learning

Zhang

2021

Scientific Programming

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Global competition is the competition of human resources, the social demand for high-quality talents is increasing, and the demand for all kinds of talents is increasing. Therefore, how to scientifically and efficiently complete the preliminary screening of college students’ mental health, so as to provide services for them, has become an important task. In order to solve the above problems, by combining the relevant professional knowledge of psychology, statistics, image processing, and artificial intelligence technology, a personality trait detection method based on active shape model (ASM) localization and deep learning is proposed. Firstly, the traditional ASM algorithm is improved and applied to facial feature point location, which provides training basis for further deep learning. It mainly includes three aspects of improvement: (1) 2D texture model based on Gabor wavelet and gradient features; (2) new multiresolution pyramid decomposition method; and (3) improved multiresolution pyramid search strategy. Secondly, the deep belief network model is used to train and classify the students’ four personality traits and facial features, so as to dig out the relationship between the four personality traits and facial features. The experimental results show that the localization effect of the improved ASM algorithm is obviously better than that of the traditional algorithm, and the classifier after learning and training has a good effect in analyzing the four personality traits.

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Robust Face Detection integrating Novel Skin Color Matching under Variant Illumination Conditions

Cited by 4 publications

References 46 publications

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

Face Skin Color Detection Method Based on YUV-KL Transform

Personality Trait Detection Based on ASM Localization and Deep Learning

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