A New Confidence Measure for Eye Detection Using Pixel Selection


KIPS Transactions on Software and Data Engineering, Vol. 4, No. 7, pp. 291-296, Jul. 2015
10.3745/KTSDE.2015.4.7.291, Full Text:

Abstract

In this paper, we propose a new confidence measure using pixel selection for eye detection and design a hybrid eye detector. For this, we produce sub-images by applying a pixel selection method to the eye patches and construct the BDA(Biased Discriminant Analysis) feature space for measuring the confidence of the eye detection results. For a hybrid eye detector, we select HFED(Haar-like Feature based Eye Detector) and MFED(MCT Feature based Eye Detector), which are complementary to each other, as basic detectors. For a given image, each basic detector conducts eye detection and the confidence of each result is estimated in the BDA feature space by calculating the distances between the produced eye patches and the mean of positive samples in the training set. Then, the result with higher confidence is adopted as the final eye detection result and is used to the face alignment process for face recognition. The experimental results for various face databases show that the proposed method performs more accurate eye detection and consequently results in better face recognition performance compared with other methods.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
Y. Lee and S. I. Choi, "A New Confidence Measure for Eye Detection Using Pixel Selection," KIPS Transactions on Software and Data Engineering, vol. 4, no. 7, pp. 291-296, 2015. DOI: 10.3745/KTSDE.2015.4.7.291.

[ACM Style]
Yonggeol Lee and Sang Il Choi. 2015. A New Confidence Measure for Eye Detection Using Pixel Selection. KIPS Transactions on Software and Data Engineering, 4, 7, (2015), 291-296. DOI: 10.3745/KTSDE.2015.4.7.291.