Title
Face Detection with High Precision Based on Radial-Symmetry Transform and Eye-Pair Checking
Abstract
In this paper, a novel face detection algorithm is proposed which can detect both face and eye pupils efficiently at the same time. Interestingly, the results of face detection can be used to identify the regions of interest for pupil detection, and the results of pupil detection can be further contributed to detect more precise faces. A cascaded face filter is first constructed by using an adaboosting algorithm, which can rapidly filter out the non-face regions and keeps the possible face regions. Based on a radial-symmetry transform, the signal of eye pupil in a face candidate is considerably enhanced and becomes easy to detect. With an eye-pair checking process, the two pupil candidates are chosen as the outputted pupils if their corresponding face image obtains the highest verification score which is also larger than a predefined threshold. Experiments on the famous BioID face database have shown that 90.0% of faces can be successfully detected, and among the detected faces about 98% of eye pupils are detected with highly acceptable precision.
Year
DOI
Venue
2006
10.1109/AVSS.2006.49
AVSS
Keywords
Field
DocType
face detection,radial-symmetry transform,pupil detection,eye pupil,face candidate,high precision,famous bioid face database,possible face region,cascaded face filter,novel face detection algorithm,eye-pair checking,precise face,corresponding face image,application software,computer science,radial symmetry,computed tomography,region of interest,computer vision,face recognition
Facial recognition system,Computer vision,AdaBoost,Object-class detection,Pattern recognition,Three-dimensional face recognition,Computer science,Symmetry in biology,Pupil,Computed tomography,Artificial intelligence,Face detection
Conference
ISBN
Citations 
PageRank 
0-7695-2688-8
3
0.56
References 
Authors
5
4
Name
Order
Citations
PageRank
Yea-shuan Huang147979.42
Hao-ying Cheng230.56
Po-Feng Cheng3151.57
Cheng-Yuan Tang44910.61