scholarly journals A Face Detection Method Based on Image Processing and Improved Adaptive Boosting Algorithm

2020 ◽  
Vol 37 (3) ◽  
pp. 395-403
Author(s):  
Wanli Zhang ◽  
Xianwei Li ◽  
Qixiang Song ◽  
Wei Lu
Keyword(s):  
Image Processing ◽  
Face Detection ◽  
Detection Method ◽  
Adaptive Boosting ◽  
Boosting Algorithm

Machine Vision Application on Science and Industry

2017 ◽  
pp. 146-179
Author(s):  
Oleg Starostenko ◽  
Claudia Cruz-Perez ◽  
Vicente Alarcon-Aquino ◽  
Viktor I. Melnik ◽  
Vera Tyrsa
Keyword(s):  
Face Detection ◽  
Recognition Rate ◽  
Adaptive Boosting ◽  
Human Face Detection ◽  
Machine Vision Application ◽  
Face Detection And Recognition ◽  
Actual Field ◽  
Boosting Algorithm ◽  
Saturated Color ◽  
Correct Recognition Rate

Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.

scholarly journals Face Detection

2020 ◽  
Vol 5 (9) ◽  
pp. 688-692
Author(s):  
Amir Nobahar Sadeghi Nam
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Face Recognition ◽  
Face Detection ◽  
Main Part ◽  
Detection Method ◽  
Input Image ◽  
Effective Algorithm ◽  
Human Faces

Face detection is one of the challenging problems in the image processing, as a main part of automatic face recognition. Employing the color and image segmentation procedures, a simple and effective algorithm is presented to detect human faces on the input image. To evaluate the performance, the results of the proposed methodology is compared with ViolaJones face detection method.

Machine Vision Application on Science and Industry

2018 ◽  
pp. 1985-2012
Author(s):  
Oleg Starostenko ◽  
Claudia Cruz-Perez ◽  
Vicente Alarcon-Aquino ◽  
Viktor I. Melnik ◽  
Vera Tyrsa
Keyword(s):  
Face Detection ◽  
Recognition Rate ◽  
Adaptive Boosting ◽  
Human Face Detection ◽  
Machine Vision Application ◽  
Face Detection And Recognition ◽  
Actual Field ◽  
Boosting Algorithm ◽  
Saturated Color ◽  
Correct Recognition Rate

Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.

Real AdaBoost face detection method based on multi-threshold

2009 ◽  
Vol 29 (8) ◽  
pp. 2098-2100
Author(s):  
Shi-ming SUN ◽  
Qing PAN ◽  
You-fang JI
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Real Adaboost

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

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