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The Role Of Ear Recognition Biometrics In Personalized Audio And Hearing Aids

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Received: 30 September 2016 / Revised: 28 November 2016 / Accepted: 9 January 2017 / Published: 21 January 2017

Most of the current ICP (Iterative Closet Point) based 3D ear recognition methods use non-linear ICP algorithms to fit 3D ear models. In this method, the matching gallery is automatically based on a number of image points and then fine-tuned using the ear point cloud. However, this approach assumes that not all of the segmented ear data points contribute well to recognition. Therefore, fragmented ear data containing a lot of noisy data may cause inconsistencies in the recognition feature. Furthermore, a good ICP fit is easily trapped in local minima without limiting the local properties. This paper proposes an efficient and fully automated 3D ear recognition method to solve these problems. The system describes the 3D ear with a local feature – the local surface variation (LSV) which is responsible for the concave and convex areas of the ground. In order to extract the unique key points, the LSV descriptor is used to remove the non-existing negative data and obtain normalized ear data. In the recognition stage, a one-step modification of the closest points is proposed using the local surface variation (ICP-LSV) algorithm, which provides additional local visual information to the ear recognition process to improved matching accuracy and computational efficiency. Inter

W3550 , 3.07 GHz workstation ( DELL T3500 , Beijing , China ) , the authors were able to extract features corresponding to the ear gallery 2.32 s using the method presented in this paper . The proposed algorithm achieves 100% recognition rate and 98.55% with 2.3% equal error (EER) in the 3D Face Database of the Chinese Academy of Sciences (CASIA-3D FaceV1, CASIA, Beijing, China, 2004) ) Notre Dame University Biometrics Data in the J2 Collection (UND-J2, University of Notre Dame, South Bend, IN, USA, 2003-2005).

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Ear-based human identification technology is a new area of research in biometric identification. Compared to conventional biometric identifiers such as fingerprints, faces, and irises, using the ear has several advantages. The ear has a rich stable structure that changes little with age and does not undergo simultaneous phase changes [1]. In addition, imaging of the ear is believed to be easy and non-invasive to collect. Therefore, the biometrics of the ear has recently received a lot of attention.

In the past years, researchers have developed several methods for ear recognition based on 2D images of the ear [2, 3, 4]. From these works, the researchers found that the 2D ear recognition methods had a great impact on the change of appearance and image. Compared to 2D ear images, 3D ear data is more sensitive to illumination and position changes. Therefore, ear recognition methods using 3D shape information have become the latest trend in the research field [5, 6, 7, 8, 9, 10].

Most of the existing 3D ear recognition methods are based on the ICP (Iterative Closet Point) algorithm. Although ICP is thought to be the optimal algorithm, it requires a good initial hard transition to ensure short-ear data and global coherence. Researchers have proposed several similar ICP-based techniques for the exploration and registration of gallery ear data [11, 12]. In these methods, local surface descriptors were used to extract key points on the ear surface that were used to estimate initial stiffness differences between gallery-probe pairs. A good fit is based on data obtained from the whole ear. However, these methods are limited by the data load and computational load of the two-step ICP algorithm. The ear region data used in most of the available methods are highly fragmented profile images, so there is a large amount of non-ear data, such as smooth facial skin data and hair data. Cleaning and normalizing the ear data is very important because extra data can cause inconsistencies in the ICP algorithm.

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In this work, LSV (Local Surface Variation) local feature is used to extract additional information from ear regions to extract key points. LSV is responsible for concave and convex areas of the surface, if the optimal value of LSV is selected, smooth skin data will be removed. In addition, most of the hair data was extracted and the earlobe was normalized using the same methods proposed. Therefore, the computational burden of the ICP algorithm is greatly reduced because the size of the normal data of the ear is one third of the size of the original data.

Using the ICP algorithm, it is easy to find the local minimum values without restricting the local features. Thus, existing two-step ICP-based matching techniques direct pair-wise imaging using critical points during complex matching. However, the two-step ICP algorithm can be very time consuming. In order to combine local image matching and global registration in the ICP algorithm, a modified algorithm named ICP using Invariant Features (ICPIF) was proposed by Sharp [13]. Compared with the traditional ICP, the relevant points are selected by the linear combination of the position and the expression distance of the ICPIF algorithm. ICPIF moves the shortest distance with less repetition than conventional ICP. Most and least bases are probably the most common variations. However, it will increase the computational burden to add two visual distances to the frequency.

The proposed identification method using a modified one-step ICP algorithm achieves better performance than the conventional fine registration method. In this work, the ICPIF algorithm, changed to the ICP-LSV algorithm, is proposed to propose the working distance of LSV only to avoid local smallness and obtain fast and accurate correlation. Therefore, the proposed method does not require complicated initial alignment of the probe-gallery pair.

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The remainder of this paper is organized as follows: An overview of related work and contributions is given in Section 2, and Section 3 presents the technical approach of ear recognition systems. Section 4 presents a series of experiments and comparisons to evaluate the performance of the system. Finally, Section 5 draws conclusions.

Current 3D ear recognition methods use either 3D ear data or a combination of 3D ear data. This section reviews popular and recent 3D ear recognition methods and describes the sections of this paper.

Detection and identification are the two most important components of a complete biometric system. This section provides a brief overview of ear detection and segmentation techniques. Ear detection and ear extraction methods are based on 2D or 3D profile images.

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One of the 3D ear detection methods was proposed by Chen and Banu, who combined 2D face images and different 3D images to detect and extract human ears [11]. Images were taken to locate potential ear regions called regions of interest (ROIs). The 3D ear shape model, which is a separate 3D contour in the helix and antihelix parts of the ear, was then matched to individual ear images using a modified ICP procedure. The root mean square (RMS) of the ROI with the error was considered as the region of the ear. [14], Abdel-Mottaleb et al

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