# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 2 Related Work and Contributions
### Ear Detection and Segmentation  
Detection and recognition are the two major components of a complete biometrics system. In this section, a summary of ear detection and segmentation approaches are provided. The existing ear detecting and ear region extracting approaches have been based on 2D or 3D profile images. One of the 3D ear detection approaches was proposed by [PERSON] and [PERSON], who combined 2D side face images and 3D profile range images to detect and extract human ears [11]. The edges were extracted to locate potential ear regions called regions-of-interest (ROIs). Then the reference 3D ear shape model, which was a set of discrete 3D vertices on the ear helix and the antihelix parts, was matched with individual ear images by following a modified ICP procedure. The ROI with the minimum Root Mean Square (RMS) error was considered to be the ear region. In a previous [14], [PERSON] and [PERSON] put forward a 3D ear recognition approach in which the ear regions were segmented by locating the ridges and ravines on the profile images. However, it may be difficult to detect the ridges and ravines when the ear is partly occluded. In another study [15], [PERSON] and [PERSON] proposed a rotation and scale invariant ear detection technique from 3D profile images using graph inherent structural details of the ear in 3D range data. [PERSON] et al. used an active contour algorithm and a tree structured graph to segment the ear region in [16]. [PERSON] and [PERSON] exploited an ear extracting approach based on ear pit detection and Active Contour Algorithm [17]. They found the ear pit using skin detection, curvature estimation, and surface segmentation and classification. Then an active contour algorithm was implemented to outline the ear region. All of the ear images on the University of Notre Dame (UND) database were correctly segmented using the combination of color and depth images in the active contour algorithm. However, since this method has to locate the nose tip and ear pit on the profile image, this algorithm may not be robust enough to pose variations or hair covering. Researchers proposed some learning algorithms to detect ears under complex background from 2D images, where corresponding 3D ear data could thenbe extracted from the co-registered 3D images if necessary. [PERSON] [12] detected ear regions on 2D profile images using a detector based on the AdaBoost algorithm. They argued that it was efficient and robust to noisy background and pose variation. [PERSON] et al. [18] modified the Adaboost algorithm and, in doing so, reduced the training time significantly. [PERSON] et al. [19] presented a two-step ear detection system utilized arc-masking candidate extraction and AdaBoost polling verification. Firstly, the ear candidates were extracted by the arc-masking edge search algorithm; then the ear was located by rough AdaBoost polling verification. [PERSON] and [PERSON] [20] used the improved AdaBoost algorithm to detect ears under complex backgrounds. They sped up the detection procedure and reported a good detection rate on three test data sets. It has been experimentally shown that the learning algorithms perform better than the algorithms based on ear edge detection or ear template matching on 2D images. However, shallow learning models such as Adaboost algorithm also lack robustness in realistic scenarios, which may contain occlusion, illumination variation, scaling, and rotation. Recently, convolution neural network (CNN) has significantly pushed forward the development of image classification and object detection [21]. [PERSON] et al. [22] proposed a new framework of object detection called Regions with CNN features (R-CNN). The R-CNN approach achieved the best result on the Pattern Analysis, Statistical modelling and Computational Learning Visual Object Classes (PASCAL VOC 2010) Challenge. Then a modified network called Faster R-CNN was proposed by [PERSON] et al. [23].