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Machine Vision Application on Science and Industry: Real-Time Face Sensing and Recognition in Machine Vision – Trends and New Advances

Machine Vision Application on Science and Industry: Real-Time Face Sensing and Recognition in Machine Vision – Trends and New Advances

Oleg Starostenko, Claudia Cruz-Perez, Vicente Alarcon-Aquino, Viktor I. Melnik, Vera Tyrsa
Copyright: © 2018 |Pages: 28
ISBN13: 9781522552048|ISBN10: 1522552049|EISBN13: 9781522552055
DOI: 10.4018/978-1-5225-5204-8.ch086
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MLA

Starostenko, Oleg, et al. "Machine Vision Application on Science and Industry: Real-Time Face Sensing and Recognition in Machine Vision – Trends and New Advances." Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 1985-2012. https://doi.org/10.4018/978-1-5225-5204-8.ch086

APA

Starostenko, O., Cruz-Perez, C., Alarcon-Aquino, V., Melnik, V. I., & Tyrsa, V. (2018). Machine Vision Application on Science and Industry: Real-Time Face Sensing and Recognition in Machine Vision – Trends and New Advances. In I. Management Association (Ed.), Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 1985-2012). IGI Global. https://doi.org/10.4018/978-1-5225-5204-8.ch086

Chicago

Starostenko, Oleg, et al. "Machine Vision Application on Science and Industry: Real-Time Face Sensing and Recognition in Machine Vision – Trends and New Advances." In Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1985-2012. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5204-8.ch086

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Abstract

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.

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