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Semi self-training beard/moustache detection and segmentation simultaneously

delete2017-02-01
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PRE
AI
N
Ngan Le *
K
Khoa Luu
C
Chenchen Zhu
M
Marios Savvides
DOI:10.1016/j.imavis.2016.07.009delete
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摘要

摘要

En 中文
This paper presents a robust, fully automatic and semi self-training system to detect and segment facial beard/moustache simultaneously in challenging facial images. Based on the observation that some certain facial areas, e.g. cheeks, do not typically contain any facial hair whereas the others, e.g. brows, often contain facial hair, a self-trained model is first built using a testing image itself. To overcome the limitation of that facial hairs in brows regions and beard/moustache regions are different in length, density, color, etc., a pre-trained model is also constructed using training data. The pre-trained model is only pursued when the self-trained model produces low confident classification results. In the proposed system, we employ the superpixel together a combination of two classifiers, i.e. Random Ferns (rFerns) and Support Vector Machines (SVM) to obtain good classification performance as well as improve time efficiency. A feature vector, consisting of Histogram of Gabor (HoG) and Histogram of Oriented Gradient of Gabor (HOGG) at different directions and frequencies, is generated from both the bounding box of the superpixel and the super pixel foreground. The segmentation result is then refined by our proposed aggregately searching strategy in order to deal with inaccurate landmarking points. Experimental results have demonstrated the robustness and effectiveness of the proposed system. It is evaluated in images drawn from three entire databases i.e. the Multiple Biometric Grand Challenge (MBGC) still face database, the NIST color Facial Recognition Technology FERET database and a large subset from Pinellas County database. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Beard/moustache
Detection and segmentation
Super pixel
Random Ferns
Support Vector Machine
Histogram of Gabor (HoG)
Histogram of Oriented Gradient of Gabor (HOGG)
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期刊

Image and Vision Computing 封面图
Image and Vision Computing
IF:
4.2
论文数:
4.1K
被引数:
6.7K

机构

C
Carnegie Mellon University
学者数:
1.4W
论文数: 1.4W
被引数: 2.7W
引用论文

引用论文

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