text: Identity recognition from ear images is an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes ear recognition technology an appealing choice for surveillance and security applications as well as related application domains. In contrast to other biometric modalities, where large datasets captured in uncontrolled settings are readily available, datasets of ear images are still limited in size and mostly of laboratory-like quality. As a consequence, ear recognition technology has not benefited yet from advances in deep learning and convolutional neural networks (CNNs) and is still lacking behind other modalities that experienced significant performance gains owing to deep recognition technology. In this paper we address this problem and aim at building a CNNbased ear recognition model. We explore different strategies towards model training with limited amounts of training data and show that by selecting an appropriate model architecture, using aggressive data augmentation and selective learning on existing (pre-trained) models, we are able to learn an effective CNN-based model using a little more than 1300 training images. The result of our work is the first CNN-based approach to ear recognition that is also made publicly available to the research community. With our model we are able to improve on the rank one recognition rate of the previous state-of-the-art by more than 25 on a challenging dataset of ear images captured from the web (a.k.a. in the wild).
  @cite_9: 
  @cite_37: Ear recognition is a new biometrics technique. Due to its unique physiological structure, position and stability, ear recognition is expected to be a promising authentication technique. In this paper, a hybrid system for classifying ear images is proposed. This system combines independent component analysis (ICA) and RBF network. The original ear image database is decomposed into linear combinations of several basic images. Then the corresponding coefficients of these combinations are fed up into RBF network instead of an original feature vector comprised of pixel values of grayscale images. The local features extraction of ICA and the adaptability of RBF neural network are combined reasonably. The robustness of the system is enhanced. The experiment results show that the recognition rate of ICA RBF method is improved substantially.
  @cite_39: An Improved Non-Negative Matrix Factorization with sparseness constraints (INMFSC) is proposed by imposing an additional constraint on the objective function of NMFSC, which can control the sparseness of both the basis vectors and the coefficient matrix simultaneously. The update rules to solve the objective function with constraints are presented. Research of ear recognition and its application is a new subject in the field of biometrics authentication. In practical application, ear is maybe partially occluded by hair etc. So the proposed INMFSC is applied on ear recognition with normal images and partially occluded images. Experiment results show that, compared with the traditional NMFSC, the proposed method not only obtains higher recognition rate, but also improves the sparseness and the orthogonality of coefficient matrix.
summary: Holistic techniques rely on the global ear appearance and exploit representations that encode the ear structure as whole. As the appearance of ears varies significantly with pose or illumination, care needs to be taken before computing holistic features from input images and normalization techniques need to be applied to correct for these changes prior to feature extraction. Examples of global techniques can be found in @cite_9 @cite_39 @cite_37 .