Ear Feature Research Articles

Force field convergence map and Log-Gabor filter based multi-view ear feature extraction

Based on force field convergence map and Log-Gabor filter, a novel multi-view ear feature extraction approach is proposed. This work first introduces the basic concepts and principles of force field transformation. Then a discussion on why force field convergence map rather than force field transformation is more suitable for multi-view ear feature extraction is given. After getting multi-view ear force field convergence map, Log-Gabor filter is applied to extract multiple scale and multiple orientation features. Finally, to verify the effectiveness of the proposed feature extraction method, different classifiers and different multi-view ear dataset are well utilized, to perform multi-view ear classification task. Experimental results and comparisons show the efficiency and the superiority of the proposed convergence map with the Log-Gabor filter method.

Ear Recognition using Dual Tree Complex Wavelet Transform

Since last 10 years, various methods have been used for ear recognition. This paper describes the automatic localization of an ear and it’s segmentation from the side poses of face images. In this paper, authors have proposed a novel approach of feature extraction of iris image using 2D Dual Tree Complex Wavelet Transform (2D-DT-CWT) which provides six sub-bands in 06 different orientations, as against three orientations in DWT. DT-CWT being complex, exhibits the property of shift invariance. Ear feature vectors are obtained by computing mean, standard deviation, energy and entropy of these six sub-bands of DT-CWT and three sub-bands of DWT. Canberra distance and Euclidian distance are used for matching. This method is implemented and tested on two image databases, UND database of 219 subjects from the University of Notre Dame and own database created at MCTE, of 40 subjects which is also used for online ear testing of system for access control at MCTE. False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) and Receiver’s Operating Curve (ROC) are compiled at various thresholds. The accuracy of recognition is achieved above 97 %.

Ear recognition based on improved 2D principal component analysis and neural network

Since the human ear feature extraction method of 2 Dimensional Principal Component Analysis (2DPCA) gets relatively bigger features dimension,which leads to poor real-time capability and lack of data storage space,the authors proposed a new approach. First of all,pretreatment of human ear pictures was completed. Then an improved 2DPCA algorithm was used to compress feature dimension. Finally,BP neural network was used in ear classification. Experimental results show that this method is of real-time and can save feature data storage space,and also maintains the recognition rate.

Perspective methods of human identification: Ear biometrics

AbstractGeometrical methods of feature extraction from ear images in order to perform human identification are presented. Geometrical approach is motivated by the actual procedures used by police and forensic experts (so-called ear otoscopy). In their work, geometrical features of ears such as size, height, width, and shapes of earlobe are useful and valid proofs of identity. The contribution of the article is development of the new and original methods of geometrical feature extraction from 2D ear images. Four novel algorithms of ear feature extraction from contour images are described in detail. Moreover, identification results obtained for each of the methods, based on the distance of feature vectors in the feature space, are presented.