To face or not to face: towards reducing false positive of face detection

Yang, Siqi, Wiliem, Arnold and Lovell, Brian C. (2017). To face or not to face: towards reducing false positive of face detection. In: International Conference Image and Vision Computing New Zealand. 2016 International Conference on Image and Vision Computing New Zealand, IVCNZ 2016, Palmerston North, New Zealand, (7-12). 21 - 22 November 2016. doi:10.1109/IVCNZ.2016.7804415


Author Yang, Siqi
Wiliem, Arnold
Lovell, Brian C.
Title of paper To face or not to face: towards reducing false positive of face detection
Conference name 2016 International Conference on Image and Vision Computing New Zealand, IVCNZ 2016
Conference location Palmerston North, New Zealand
Conference dates 21 - 22 November 2016
Convener IEEE
Proceedings title International Conference Image and Vision Computing New Zealand
Journal name International Conference Image and Vision Computing New Zealand
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2017
Sub-type Fully published paper
DOI 10.1109/IVCNZ.2016.7804415
Open Access Status Not yet assessed
ISBN 9781509027484
9781509027477
9781509027491
ISSN 2151-2205
Start page 7
End page 12
Total pages 6
Collection year 2018
Language eng
Abstract/Summary We tackle the problem of reducing the false positive rate of face detectors by applying a classifier after the detection step. We first define and study this post classification problem. To this end, we first consider the multiple-stage cascade structure which is the most common face detection architecture. Here, each cascade stage aims to solve a binary classification problem, denoted the Face/non-Face (FnF) problem. In this context, the post classification problem can be considered as the most challenging FnF problem, or the Hard FnF (HFnF) problem. To study the HFnF problem, we propose HFnF datasets derived from the recent face detection datasets. A baseline method utilizing the GIST features and Support Vector Machine (SVM) classifier is also proposed. In our evaluation, we found that it is possible to further improve the face detection performance by addressing the HFnF problem.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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