Face image profiles features extraction for recognition systems
Open Access
- 4 February 2021
- journal article
- Published by Ukrainian National Forestry University in Scientific Bulletin of UNFU
- Vol. 31 (1), 117-121
- https://doi.org/10.36930/40310120
Abstract
The object of research is the algorithm of piecewise linear approximation when applying it to the selection of facial features and compression of its images. One of the problem areas is to obtain the optimal ratio of the degree of compression and accuracy of image reproduction, as well as the accuracy of the obtained facial features, which can be used to search for people in databases. The main characteristics of the image of the face are the coordinates and size of the eyes, mouth, nose and other objects of attention. Dimensions, distances between them, as well as their relationship also form a set of characteristics. A piecewise linear approximation algorithm is used to identify and determine these features. First, it is used to approximate the image of the face to obtain a graph of the silhouette from right to left and, secondly, to approximate fragments of the face to obtain silhouettes of the face from top to bottom. The purpose of the next stage is to implement multilevel segmentation of the approximated images to cover them with rectangles of different intensity. Due to their shape they are called barcodes. These three stages of the algorithm the faces are represented by two barcode images are vertical and horizontal. This material is used to calculate facial features. The medium intensity function in a row or column is used to form an approximation object and as a tool to measure the values of facial image characteristics. Additionally, the widths of barcodes and the distances between them are calculated. Experimental results with faces from known databases are presented. A piecewise linear approximation is used to compress facial images. Experiments have shown how the accuracy of the approximation changes with the degree of compression of the image. The method has a linear complexity of the algorithm from the number of pixels in the image, which allows its testing for large data. Finding the coordinates of a synchronized object, such as the eyes, allows calculating all the distances between the objects of attention on the face in relative form. The developed software has control parameters for conducting research.Keywords
This publication has 10 references indexed in Scilit:
- Face image barcodes by distributed cumulative histogram and clusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2020
- Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithmsEvolutionary Intelligence, 2017
- Handbook of Face RecognitionPublished by Springer Science and Business Media LLC ,2011
- Face RecognitionPublished by IntechOpen ,2010
- Recent Advances in Face RecognitionPublished by IntechOpen ,2008
- Multilevel Thresholding Methods for Image Segmentation with Otsu Based on QPSOPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Multilevel thresholding for image segmentation through a fast statistical recursive algorithmPattern Recognition Letters, 2007
- Image segmentation by histogram thresholding using hierarchical cluster analysisPattern Recognition Letters, 2006
- ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURECartographica: The International Journal for Geographic Information and Geovisualization, 1973
- An iterative procedure for the polygonal approximation of plane curvesComputer Graphics and Image Processing, 1972