种族分类的机器学习方法:英国巴基斯坦面孔

S. Jilani, H. Ugail, A. M. Bukar, Andrew Logan, T. Munshi
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引用次数: 11

摘要

种族是面对身份最显著的线索之一。分析特定种族的面部数据是一个具有挑战性的问题,主要使用基于计算机的算法进行。目前发表的文献集中在正面面部图像的使用上。我们使用侧面面部图像解决了二元(英国、巴基斯坦或其他种族)种族分类的挑战。提出的框架是基于在135个多民族和多种族受试者和总共675张人脸图像的专门构建的新数据库中,使用10个人体测量面部地标提取几何特征。利用主成分分析和偏最小二乘回归对图像进行降维。使用线性支持向量机进行分类。该框架使用PCA算法+ SVM作为分类器的种族分类准确率为71.11%,使用PLS算法+ SVM作为分类器的种族分类准确率为76.03%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Machine Learning Approach for Ethnic Classification: The British Pakistani Face
Ethnicity is one of the most salient clues to face identity. Analysis of ethnicity-specific facial data is a challenging problem and predominantly carried out using computer-based algorithms. Current published literature focusses on the use of frontal face images. We addressed the challenge of binary (British Pakistani or other ethnicity) ethnicity classification using profile facial images. The proposed framework is based on the extraction of geometric features using 10 anthropometric facial landmarks, within a purpose-built, novel database of 135 multi-ethnic and multi-racial subjects and a total of 675 face images. Image dimensionality was reduced using Principle Component Analysis and Partial Least Square Regression. Classification was performed using Linear Support Vector Machine. The results of this framework are promising with 71.11% ethnic classification accuracy using a PCA algorithm + SVM as a classifier, and 76.03% using PLS algorithm + SVM as a classifier.
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