Open AccessApplication of Ear Detection in Non-frontal Face Recognition
Author(s) -
Yadong Luo,
Tong Shen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1607/1/012097
Subject(s) - artificial intelligence , computer science , adaboost , computer vision , face detection , face (sociological concept) , ellipse , pattern recognition (psychology) , zoom , haar like features , facial recognition system , mathematics , classifier (uml) , engineering , social science , geometry , sociology , petroleum engineering , lens (geology)
Face gesture recognition is a research hotspot in the field of machine vision and pattern recognition.In this paper, we use the Haar-Like feature-based Gentle Adaboost algorithm to detect the ear of non-frontal face. An ear sample library for ear detection of non-frontal face images was constructed. Through zoom detection window and determining the ROI area, it can successfully detect ear and work better than the basic Adaboost algorithm. The detection rate of the algorithm in the experiment reached 82%. It is suitable for most of the non-frontal faces we photographed.It still has a good detection effect for images with a small amount of hair or eyeglass frame interference.Based on the contour information of the ear, an approximate method for determining the lower end of the ear based on edge detection and a head and neck boundary determination algorithm based on the lower end of the human ear are proposed, which is more complete than the previous algorithm based on the determination of the concave points on both sides of the neck. The face area is segmented to make the face contour finally applied on the fitted ellipse more accurate.