A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

Yang, Xiaoying; Liang, Nannan; Zhou, Wei; Lü, Hongmei

doi:10.18280/ts.370606

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…The research of face detection has important research value due to the variability of facial expression, skin color, and illumination. Yong and Yanru [1] studied the face detection based on skin color features and found that the difference in skin color is obvious under different illumination levels. In order to solve this problem, it uses YCbCr and HIS two skin color space lighting as the technical basis.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor

Yan

2021

Journal of Sensors

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With the continuous innovation of network technology, various kinds of convenient network technologies have grown, and human dependence on network technology has gradually increased, which has resulted in the importance of network information security issues. With the continuous development of my country’s industrialization, the application of sensors is becoming more and more extensive, for example, the security vulnerabilities and defects in the operating system itself. Traditional sensors can “perceive” a certain thing or signal, convert it into an electrical signal and record it, and then use a conversion circuit to output the electrical signal into a value or other display form that is conducive to observation. Nowadays, sensors have been further developed. Based on the original “perception” function, combined with computer technology, it integrates data storage, data processing, data communication, and other functions, so that it has analysis functions and can better display information. The technical level has reached a new level. Early intelligent recognition mainly used the uniqueness of finger and palm lines to scan and contrast, but due to some weather reasons or skin texture constraints caused by skin texture, these methods showed certain limitations. This paper proposes a new computer vision-based algorithm from face detection technology and face recognition technology. In the face detection technology, it is mainly introduced from the OpenCV method. Face recognition technology is improved in practical applications through the Seetaface method and YouTu method. At the same time, using the contrast experiment, the detection and recognition rates under the three different requirements of side face detection, occlusion detection, and facial exaggerated expression are compared, and the accuracy of each method is improved. The results show that each case is compared in each case. The advantages and disadvantages of the algorithm effectively verify the effectiveness of the method.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor

Yan

2021

Journal of Sensors

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“…Face detection [10] is a critical phase in this framework. The initial stage of the face mask detection system is to detect faces in stored and real-time images.…”

Section: Face Detector Ssd Modelmentioning

confidence: 99%

Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

Jayaswal¹,

Dixit²

2021

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A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.

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“…In this paper, the AdaBoost [13] algorithm is chosen as the classifier. The algorithm first trains several weak classifiers with the sample set, and then combines these weak classifiers into a strong classifier.…”

Section: Figure 2 Calculation Process Of Lbp Featurementioning

confidence: 99%

Recognition and Positioning of Container Lock Holes for Intelligent Handling Terminal Based on Convolutional Neural Network

Wang¹

2021

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Container handling is a key link in container transport. In an automated handling terminal, the work efficiency directly depends on the time cost of the alignment between the spreader and the lock holes of the container. This paper attempts to improve the recognition and location of container lock holes with the aid of machine vision. Firstly, a lock hole recognition algorithm was designed based on local binary pattern (LBP) feature and classifier. After feature extraction and classifier training, multi-scale sliding window was used to recognize each lock hole. To realize real-time, accurate recognition of lock holes, the convolutional neural network (CNN) with improved threshold was incorporated to our algorithm. The tests on actual datasets show that our algorithm can effectively locate container lock holes.

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A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

Cited by 14 publications

References 22 publications

[Retracted] Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor

[Retracted] Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor

Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

Recognition and Positioning of Container Lock Holes for Intelligent Handling Terminal Based on Convolutional Neural Network

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