Partial Data Ear Recognition From One Sample per Person
Long Chen, Zhichun Mu · IEEE Transactions on Human-Machine Systems · 2016
The relatively stable structure of the human ear makes it suitable for identification. The significance of ear recognition in human authentication has become prominent in recent years. A number of ear recognition systems and methods have achieved good performance under limited conditions in the laboratory. In real-world applications, however, such as passport identification and law enforcement, where usually only one sample per person (OSPP) is registered in the gallery, most of the existing ear recognition methods are paralyzed by partial data (e.g., pose variations and occlusion). To address such problems, we propose a weighted multikeypoint descriptor sparse representation-based classification method to use local features of ear images. By adding adaptive weights to all the keypoints on a query image, the intraclass variations are reduced. Besides, the interclass variations of the gallery samples are enlarged by purifying the multikeypoint dictionary. Experiments are carried out on two benchmark databases, i.e., the Indian Institute of Technology Delhi ear database and the University of Science and Technology Beijing ear image database III, to demonstrate the feasibility and effectiveness of the proposed method in dealing with partial data problems in ear recognition under the premise of OSPP in the gallery. The proposed method has achieved state-of-the-art recognition performance especially when the ear images are affected by pose variations and random occlusion.