基于肤色和最大熵的粒子群算法的人脸定位

S. Jois, Rakshith Ramesh, A. Kulkarni
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引用次数: 7

摘要

人脸定位是人脸识别系统中最重要的预处理操作之一。人们越来越需要能够快速生成面部周围感兴趣的正确区域的算法。本文提出了一种基于粒子群优化的皮肤检测人脸定位算法。其中适应度函数的最小化是用来解决优化问题的。在这里,边界框被建模为粒子,或随机分布的测试向量,以确定图像中可能存在人脸的感兴趣区域,适应度函数返回适应度值,该适应度函数测试盒子内部的最大熵和最大皮肤特征,这两者结合起来表明内部代表人脸的可能性。因此,选择具有最佳适应度值的粒子(边界框)作为图像的人脸区域,并将其输出馈送到基于二元粒子群优化的人脸识别系统中,从而获得比传统系统更高的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face localization using skin colour and maximal entropy based particle swarm optimization for facial recognition
One amongst the vital pre-processing operation in every Face Recognition System is Face localization. There is a growing need for algorithms that quickly yield the correct region of interest around the face. Here an algorithm is proposed which performs skin detection using Particle Swarm Optimization for face localization. Where minimization of fitness function is used to resolve optimization problems. Here bounding boxes are modelled as particles, or randomly distributed test vectors to ascertain for the region of interest in an image where it is likely that the face exists, with the fitness value returned by the fitness function that tests the interior of a box for maximum entropy and maximum skin like features, which in combination indicates how likely the interior represents a face. Consequently, the particle (bounding box) with the best fitness value is chosen as face region of the image, and the output is fed to a Binary Particle Swarm Optimization based Face Recognition System therefore yielding higher recognition rates than conventional systems.
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