Book ChapterDOI
Using ear biometrics for personal recognition
Li Yuan,Zhichun Mu,Zhengguang Xu +2 more
TLDR
Feasibility and characteristics of ear recognition was discussed, recent advances in 2D and 3D domain was presented and a proposal for future research topics, such as ear database generation, ear detection, ear occluding problem and multimodal biometrics with face etc.Abstract:
Application and research of ear recognition technology is a new subject in the field of biometrics recognition. Earlier research showed that human ear is one of the representative human biometrics with uniqueness and stability. Feasibility and characteristics of ear recognition was discussed and recent advances in 2D and 3D domain was presented. Furthermore, a proposal for future research topics was given, such as ear database generation, ear detection, ear occluding problem and multimodal biometrics with face etc.read more
Citations
More filters
Journal ArticleDOI
Robust ear identification using sparse representation of local texture descriptors
Ajay Kumar,Tak-Shing T. Chan +1 more
TL;DR: This paper investigates a new approach for more accurate ear recognition and verification problem using the sparse representation of local gray-level orientations and presents experimental results from publically available UND and IITD ear databases which achieve significant improvement in the performance.
Journal ArticleDOI
Ear recognition based on local information fusion
Li Yuan,Zhi chun Mu +1 more
TL;DR: A 2D ear recognition approach based on local information fusion to deal with ear recognition under partial occlusion is proposed and results have illustrated that using only few sub-windows the authors can represent the most meaningful region of the ear, and the multi-classifier model gets higher recognition rate than using the whole image for recognition.
Proceedings ArticleDOI
Multimodal biometric system using face, ear and gait biometrics
TL;DR: A novel multimodal biometric recognition system using three modalities including face, ear and gait, based on Gabor+PCA feature extraction method with fusion at matching score level with excellent recognition performance and outperforms unimodal systems is proposed.
Journal ArticleDOI
An ear biometric system based on artificial bees and the scale invariant feature transform
TL;DR: The obtained results show that the proposed approach outperforms traditional ear image contrast enhancement techniques, and increases the amount of detail in the ear image, and consequently improves the recognition rate.
Proceedings ArticleDOI
Ear Detection Based on Skin-Color and Contour Information
Li Yuan,Zhi-Chun Mu +1 more
TL;DR: A tracking method which combines both skin-color model and intensity contour information is proposed to detect and track the human ear in a sequence frames and can meet the real-time requirement and works well in practical situations.
References
More filters
Journal ArticleDOI
Face recognition: A literature survey
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
XM2VTSDB: The Extended M2VTS Database
TL;DR: This poster presents a poster presenting a probabilistic procedure for estimating the response of the immune system to laser-spot assisted surgery to treat central giant cell granuloma.
Proceedings ArticleDOI
Multimodal biometrics: An overview
Arun Ross,Anil K. Jain +1 more
TL;DR: The various scenarios that are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies that can be adopted to consolidate information are discussed.
Journal ArticleDOI
Comparison and combination of ear and face images in appearance-based biometrics
TL;DR: It is found that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent in one experiment and multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric.
Journal ArticleDOI
Human Ear Recognition in 3D
TL;DR: The experimental results on the UCR data set of 155 subjects with 902 images under pose variations and the University of Notre Dame dataSet of 302 subjects with time-lapse gallery-probe pairs are presented to compare and demonstrate the effectiveness of the proposed algorithms and the system.