Ear recognition based on Gabor features and KFDA.
We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on improved Adaboost algorithm detects the ear part under complex background using two steps: offline cascaded classifier training and online ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature extraction and orientation selection, Gabor filter based ear feature extraction is presented in this paper. Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. Finally distance based classifier is applied for ear recognition. Experimental results of ear recognition on two datasets (USTB and UND datasets) and the performance of the ear authentication system show the feasibility and effectiveness of the proposed approach.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ; Visualization and Intelligent Systems Laboratory, University of California Riverside, Riverside, CA, 92507, USA.
Li
0000-0001-9988-072X
L
Yuan
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Zhichun
Z
Mu
D000465:Algorithms; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D004423:Ear / Q000033:anatomy & histology*; D005145:Face / Q000033:anatomy & histology; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted