Biometric Recognition Using Deep Learning: A Survey <s> Ear Recognition <s> In this paper we address for the first time, the problem of user identification using ear biometrics in the context of sparse representation. During the training session the compressed ear images are transformed to vectors to develop a dictionary matrix A [1]. The downsampled probe vector y is used to develop a linear, underdetermined system of equation y = Ax, x being unknown. The ill-posed system is regularized by utilizing the sparse nature of x and the inverse problem is solved through the l 1 -norm minimization. Ideally the nonzero entries in the recovered vector x correspond to the class of the probe y. The developed system does not assume any preprocessing or normalization of the ear region. We did extensive experiments on the UND [2,3] and the FEUD [4] databases with session variability and incorporating different head rotations and lighting conditions. The proposed system is found to be robust under varying light and head rotations yielding a high recognition rate of the order of 98%. Moreover, in context of sparse representation a tuning parameter of the system is identified and is designated as an Operating Point (OP). The significance of the OP is highlighted by mathematical arguments and experimental verifications. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Ear Recognition <s> 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 convolutionalneural 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 1300training 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). <s> BIB002
Ear recognition is a more recent problem that scientists are exploring, and the volume of biometric recognition works involving ears is expected to increase in the coming years. One of the more prominent aspects of ear recognition is the fact that the subject can be photographed from either side of their head and the ears are almost identical (suitable when subject is not cooperating, or hiding his/her face). Also, since there is no need for the subject's proximity, images may be taken from the ear more easily. However, ears of the subject may still be occluded by factors such as hair, hat, and jewelry, making it difficult to detect and use the ear image  . There are multiple classical methods to perform ear recognition: geometric methods, which try to extract the shape of the ear; holistic methods, which extract the features from the ear image as a whole; local methods, which specifically use a portion of the image; and hybrid methods, which use a combination of the others BIB002 , BIB001 .