Face image classification by pooling raw features

Shen, Fumin, Shen, Chunhua, Zhou, Xiang, Yang, Yang and Shen, Heng Tao (2016) Face image classification by pooling raw features. Pattern Recognition, 54 94-103. doi:10.1016/j.patcog.2016.01.010

Author Shen, Fumin
Shen, Chunhua
Zhou, Xiang
Yang, Yang
Shen, Heng Tao
Title Face image classification by pooling raw features
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
Publication date 2016-01-22
Sub-type Article (original research)
DOI 10.1016/j.patcog.2016.01.010
Open Access Status Not Open Access
Volume 54
Start page 94
End page 103
Total pages 25
Place of publication Elsevier
Publisher Amsterdam, Netherlands
Collection year 2017
Language eng
Formatted abstract
We propose a very simple, efficient yet surprisingly effective feature extraction method for face recognition (about 20 lines of Matlab codes), which is mainly inspired by spatial pyramid pooling in generic image classification. We show that, coupled with a linear classifier, features formed by simply pooling local patches over a multi-level pyramid can achieve state-of-the-art performance on face recognition. The simplicity of our feature extraction procedure is demonstrated by the fact that no learning is involved (except PCA whitening). It is shown that, multi-level spatial pooling and dense extraction of multi-scale patches play critical roles in face image classification. The extracted facial features can capture strong structural information of individual faces with no label information being used. We also find that, pre-processing on local image patches such as contrast normalization can have an important impact on the classification accuracy. In particular, on the challenging face recognition datasets of FERET and LFW-a, our method improves previous best results by large gaps. Promising results are also achieved on the general image classification database Caltech-101.
Keyword Face recognition
Image classification
Feature pooling
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Article in Press. Accepted manuscript

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Created: Mon, 25 Jan 2016, 10:55:44 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering