Robust Face Detection for Low-Resolution Images

Shinji Hayashi; Osamu Hasegawa

doi:10.20965/jaciii.2006.p0093

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JACIII Vol.10 No.1 pp. 93-101

doi: 10.20965/jaciii.2006.p0093

(2006)

Paper:

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Robust Face Detection for Low-Resolution Images

Shinji Hayashi^, and Osamu Hasegawa^,**

^*Tokyo Institute of Technology, R2-52, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan

^**PRESTO, Japan Science and Technology Agency (JST)

Received:

April 22, 2005

Accepted:

June 27, 2005

Published:

January 20, 2006

Keywords:

face detection, low resolution

Abstract

Face detection, one of the most actively researched and progressive computer vision fields, has been little studied in low-resolution images. Using the AdaBoost-based face detector and MIT+CMU frontal face test set – the standard detector and images for evaluation in face detection – we found that face detection rate falls to 39% from 88% as face resolution decreases from 24×24 pixels to 6×6 pixels. We discuss a proposal using “portrait images,” “image expansion,” “frequency-band limitation of features” and “two-detector integration” and show that 71% of face detection rate is obtained for 6×6 pixel faces of MIT+CMU frontal face test set. Note that each of the above detections involves 100 false positives for 112 evaluation images.

Cite this article as:

Shinji Hayashi and Osamu Hasegawa, “Robust Face Detection for Low-Resolution Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.1, pp. 93-101, 2006.

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References

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[1] [1] M. H. Yang, D. J. Kriegman, and N. Ahuja, “Detecting Faces in images: A Survey,” IEEE Trans. on PAMI, Vol.24, No.1, pp. 34-58, January, 2002.

[2] [2] H. Schneiderman, “Feature-Centric Evaluation for Efficient Cascaded Object Detection,” in Proc. of CVPR, Vol.2, pp. 29-36, June, 2004.

[3] [3] M. Osadchy, and D. Karen, “Image Detection Under Varying Illumination and Pose,” in Proc. of ICCV, Vol.II, pp. 668-673, July, 2001

[4] [4] B. Wu, H. Ai, C. Huang, and S. Lao, “Fast Rotation Invariant Multiview Face Detection Based on Real Adaboost,” in Proc. of FGR, pp. 79-84, May, 2004.

[5] [5] A. Torralba, and P. Shina, “Detecting Faces in Impoverished Images,” AI Memo 2001-028, CBCL Memo 208, 2001.

[6] [6] P. Viola, and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” in Proc. of CVPR, Vol.1, pp. 511-518, December, 2001.

[7] [7] R. Lienhart, A. Kuranov, and V. Pisarevsky, “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection,” in Proc. of DAGM’03, pp. 297-304, September, 2003.

[8] [8] S. Z. Li, L. Zhu, Z. Q. Zhang, A. Blake, H. J. Zhang, and H. Shum, “Statistical Learning of Multi-View Face Detection,” in Proc. of ECCV, pp. 67-81, May, 2002.

[9] [9] C. Liu, and H. Y. Shum, “Kullback-Leibler Boosting,” in Proc. of CVPR, Vol.1, pp. 587-594, June, 2003.

[10] [10] H. A. Rowley, S. Baluja, and T. Kanade, “Neural Network-Based Face Detection,” IEEE Trans. on PAMI, Vol.20, pp. 23-38, January, 1998.

Robust Face Detection for Low-Resolution Images

Shinji Hayashi*, and Osamu Hasegawa*,**

Shinji Hayashi^, and Osamu Hasegawa^,**