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).
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 .
@cite_9 @cite_37 @cite_39
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The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features. The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people. Owing to these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, the authors categorise and summarise approaches to ear detection and recognition in 2D and 3D images. Then, they provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which they consider to be the most important application of ear recognition characteristic in the near future. Ear is a new class of relatively stable biometric that is invariant from childhood to old age. It is not affected with facial expressions, cosmetics and eye glasses. Human ear is one of the representative human biometrics with uniqueness and stability. Ear Recognition for Personal Identification using 2-D ear from a side face image is a challenging problem. This paper analyzes the efficiency of using texture features such as Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor Filter for the recognition of ears. The combination of three feature vectors was experimented with. It is found that the combination gives better results compared to when the features were used in isolation. Further, it is found that the recognition accuracy improves by extracting local texture features extracted from sub-images. The proposed technique is tested using an ear database which contains 442 ear images of 221 subjects and obtained 94.12 recognition accuracy. Monocular head pose estimation requires learning a model that computes the intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human face. Annotating ground truth head pose angles for images in the wild is difficult and requires ad-hoc fitting procedures (which provides only coarse and approximate annotations). This highlights the need for approaches which can train on data captured in controlled environment and generalize on the images in the wild (with varying appearance and illumination of the face). Most present day deep learning approaches which learn a regression function directly on the input images fail to do so. To this end, we propose to use a higher level representation to regress the head pose while using deep learning architectures. More specifically, we use the uncertainty maps in the form of 2D soft localization heatmap images over five facial keypoints, namely left ear, right ear, left eye, right eye and nose, and pass them through an convolutional neural network to regress the head-pose. We show head pose estimation results on two challenging benchmarks BIWI and AFLW and our approach surpasses the state of the art on both the datasets.