Biometric Recognition Using Deep Learning: A Survey <s> AWE Ear Dataset: <s> The biometric identification of persons became a very important problem nowadays. Various aspects of criminality, and lately terrorism, forced us to increase the financial outlays allocated for safety measures based on biometrics. Fingerprints and face are the most popular biometric features. However, new modalities are still desirable (moreover, nowadays the usage of multimodal systems becomes very popular). An ear seems to be a very interesting one. The auricle has complex and stable structure and is as distinguishable for various persons as the face. During the development of biometric algorithms their proper and reliable testing is one of the most important aspects. For that purpose a test database can be very helpful. There are many test bases with faces, but ear databases are very rare. Moreover, the existing ones are usually strongly limited. Therefore, in this paper a new ear images database (the West Pomeranian University of Technology Ear Database) is presented for usage in various scientific applications, e.g. for testing biometric algorithms. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> AWE Ear Dataset: <s> In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions. The goal of the challenge was to assess the performance of existing ear recognition techniques on a challenging large-scale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques for the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity of the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer of the UERC was found to ensure robust performance on a smaller part of the dataset (with 180 subjects) regardless of image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> AWE Ear Dataset: <s> Here, the authors have extensively investigated the unconstrained ear recognition problem. The authors have first shown the importance of domain adaptation, when deep convolutional neural network (CNN) models are used for ear recognition. To enable domain adaptation, the authors have collected a new ear data set using the Multi-PIE face data set, which they named as Multi-PIE ear data set. The authors have analysed in depth the effect of ear image quality, for example, illumination and aspect ratio, on the classification performance. Finally, the authors have addressed the problem of data set bias in the ear recognition field. Experiments on the UERC data set have shown that domain adaptation leads to a significant performance improvement. For example, when VGG-16 model is used and the domain adaptation is applied, an absolute increase of around 10% has been achieved. Combining different deep CNN models has further improved the accuracy by 4%. In the experiments that the authors have conducted to examine the data set bias, given an ear image, they were able to classify the data set that it has come from with 99.71% accuracy, which indicates a strong bias among the ear recognition data sets. <s> BIB003
This database contains 1,000 images of 100 persons. Images were collected from the web using a semi-automatic procedure, and contain the following annotations: gender, ethnicity, accessories, occlusions, head pitch, head roll, head yaw, head side, and central tragus point  . Multi-PIE Ear Dataset: This dataset was created in 2017 BIB003 based on the Multi-PIE face dataset  . There are 17,000 ear images extracted from the profile and near-profile images of 205 subjects present in the face dataset. The ears in the images are in different illuminations, angles, and conditions, making it a decent dataset for a more generalized ear recognition approach. USTB Ear Database: This dataset contains ear images of 60 volunteers captured in 2002 [180] . Every volunteer is photographed three different images. They are normal frontal image, frontal image with trivial angle rotation and image under different lighting condition. UERC Ear Dataset: The ear images in this dataset BIB002 are collected from the Internet in unconstrained conditions, i.e., from the wild. There is a total of 11,804 images from 3,706 subjects, of which 2,304 images from 166 subjects are for training, and the rest are for testing. AMI Ear Dataset: This dataset  contains 700 images of size 492 x 702 from 100 subjects in the age range of 19 to 65 years old. The images are all in the same lighting condition and distance, and from both sides of the subject's head. The images, however, differ in focal lengths, and the direction the subject is looking (up, down, left, right). CP Ear Dataset: One of the older datasets in this area, the Carreira-Perpinan dataset  contains 102 left ear images taken from 17 subjects in the same conditions. WPUT Ear Dataset: The West Pomeranian University of Technology (WPUT) dataset BIB001 contains 2,071 images from 501 subjects (247 male and 254 female subjects), from different age groups and ethnicities. The images are taken in different lighting conditions, from various distances and two angles, and include ears with and without accessories, including earrings, glasses, scarves, and hearing aids.