Development of technique for face detection in image based on binarization, scaling and segmentation methods
| Autor: | Eugene Fedorov, Tetyana Utkina, Olga Nechyporenko, Yaroslav Korpan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
020209 energy ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Energy Engineering and Power Technology 02 engineering and technology Industrial and Manufacturing Engineering face detection Management of Technology and Innovation lcsh:Technology (General) 021105 building & construction 0202 electrical engineering electronic engineering information engineering lcsh:Industry Environmental Chemistry density clustering Segmentation image Electrical and Electronic Engineering Face detection Pixel business.industry Applied Mathematics Mechanical Engineering Binary image scaling segmentation Pattern recognition Image segmentation Filter (signal processing) Computer Science Applications Control and Systems Engineering Computer Science::Computer Vision and Pattern Recognition Face (geometry) lcsh:T1-995 lcsh:HD2321-4730.9 Artificial intelligence binarization Fast wavelet transform business Food Science |
| Zdroj: | Eastern-European Journal of Enterprise Technologies, Vol 1, Iss 9 (103), Pp 23-31 (2020) |
| ISSN: | 1729-4061 1729-3774 |
| Popis: | A technique for face detection inthe image is proposed, which is based on binarization, scaling, and segmentation of the image, followed by the determination of the largest connected component that matches the image of the face. Modern methods of binarization, scaling, and taxonomic image segmentation have one or more of the following disadvantages: they have a high computational complexity; require the determination of parameter values. Taxonomic image segmentation methods may have additional disadvantages: they do not allow noise and outliers selection; clusters can’t have different shapes and sizes, and their number is fixed. Due to this, to improve the efficiency of face detection techniques, the methods of binarization, scaling and taxonomic segmentation needs to be improved. A binarization method is proposed, the distinction of which is the use of the image background. This allows to simplify the process of scaling and segmentation (since all the pixels in the background are represented by the same color), non-uniform brightness of the face, and not to use the threshold settings and additional parameters. A binary image scaling method is proposed, the distinction of which is the use of an arithmetic mean filter with threshold processing and fast wavelet transform. This allows to speed up the image segmentation process by about P2times, where P is the scaling parameter, and not to use the time-consuming procedure for determining. A binary scaled image segmentation method is proposed, the distinction of which is the use of density clustering. This allows to separate areas of the face of non-uniform brightness from the image background, noise and outliers. It also allows clusters to have different shapes and sizes, to not require setting the number of clusters and additional parameters. To determine the scaling parameter, numerous studies were conducted in this work, which concluded that the dependence of the segmentation time on the scaling parameter is close to exponential. It was also found that for small P, where P is the scaling parameter, the quality of face detection deteriorates slightly. The proposed technique for face detection in image based on binarization, scaling and segmentation can be used in intelligent computer systems for biometric identification of a person by the face image |
| Databáze: | OpenAIRE |
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