文章基本信息

标题：Invariant Feature Extraction for Component-based Facial Recognition
本地全文：下载
作者：Adam Hassan ; Serestina Viriri
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：3
DOI：10.14569/IJACSA.2020.0110386
出版社：Science and Information Society (SAI)
摘要：This paper proposes an alternative invariant feature extraction technique for facial recognition using facial compo-nents. Can facial recognition over age progression be improved by analyzing individual facial components? The individual facial components: eyes, mouth, nose, are extracted using face landmark points. The Histogram of Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the individually de-tected facial components, followed by random subspace principal component analysis and cosine distance. One of the preprocessing steps implemented is the facial image alignment using angle of inclination. The experimental results show that facial recognition over age progression can be improved by analyzing individual facial components. The entire facial image can change over time, but appearance of some individual facial components is invariant.
关键词：Invariant features; facial components; facial recog-nition; age progression; HOG; LBP