TY - GEN
T1 - Scale-adaptive EigenEye for fast eye detection in wild web images
AU - Zhou, Xu
AU - Wang, Yilin
AU - Zhang, Peng
AU - Li, Baoxin
N1 - Funding Information:
This work was supported in part by a National Science Foundation grant (#1135616). Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of NSF.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/3
Y1 - 2016/8/3
N2 - Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as on-line images. This paper presents an effort on developing a fast and accurate eye detector for on-line images for which the acquisition condition is unknown and varies from one image to another, resulting in unpredictable background and variable scales for the eyes/faces. The key idea is to develop a scale-adaptive EigenEye approach, which employs an approximate scale estimated from face detection to modulate the pre-trained EigenEye basis in searching for the best match in a test image. The effort also includes building a 2845-image dataset with accurately-annotated eye locations and size, which will be made public to the community for future comparative study. Evaluation using this dataset, with comparison with a few leading state-of-the-art approaches, demonstrates the advantages of the proposed method.
AB - Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as on-line images. This paper presents an effort on developing a fast and accurate eye detector for on-line images for which the acquisition condition is unknown and varies from one image to another, resulting in unpredictable background and variable scales for the eyes/faces. The key idea is to develop a scale-adaptive EigenEye approach, which employs an approximate scale estimated from face detection to modulate the pre-trained EigenEye basis in searching for the best match in a test image. The effort also includes building a 2845-image dataset with accurately-annotated eye locations and size, which will be made public to the community for future comparative study. Evaluation using this dataset, with comparison with a few leading state-of-the-art approaches, demonstrates the advantages of the proposed method.
KW - Eye detection
KW - Feature extraction
KW - Object detection
KW - Scale adaptive
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U2 - 10.1109/ICIP.2016.7532892
DO - 10.1109/ICIP.2016.7532892
M3 - Conference contribution
AN - SCOPUS:85006717057
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2911
EP - 2915
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -