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Journal Article
Tiny and Blurred Face Alignment for Long Distance Face Recognition
- Authors
- Kyu-Dae Ban, Jaeyeon Lee, DoHyung Kim, Jaehong Kim, Yun Koo Chung
- Issue Date
- 2011-04
- Citation
- ETRI Journal, v.33, no.2, pp.251-258
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- Applying face alignment after face detection exerts a heavy influence on face recognition. Many researchers have recently investigated face alignment using databases collected from images taken at close distances and with low magnification. However, in the cases of home-service robots, captured images generally are of low resolution and low quality. Therefore, previous face alignment research, such as eye detection, is not appropriate for robot environments. The main purpose of this paper is to provide a new and effective approach in the alignment of small and blurred faces. We propose a face alignment method using the confidence value of Real-AdaBoost with a modified census transform feature. We also evaluate the face recognition system to compare the proposed face alignment module with those of other systems. Experimental results show that the proposed method has a high recognition rate, higher than face alignment methods using a manually-marked eye position. © 2011 ETRI.
- KSP Keywords
- Alignment method, Census Transform, Confidence value, Face alignment, Face recognition system, Long distance, Low quality, Real-Adaboost, Recognition Rate, Service robots, eye detection