On Shape-Mediated Enrolment in Ear Biometrics

Ears are a new biometric with major advantage in that they appear to maintain their shape with increased age. Any automatic biometric system needs enrolment to extract the target area from the background. In ear biometrics the inputs are often human head profile images. Furthermore ear biometrics is concerned with the effects of partial occlusion mostly caused by hair and earrings. We propose an ear enrolment algorithm based on finding the elliptical shape of the ear using a Hough Transform (HT) accruing tolerance to noise and occlusion. Robustness is improved further by enforcing some prior knowledge. We assess our enrolment on two face profile datasets; as well as synthetic occlusion

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations

Computer vision, archaeological classification and China's terracotta warriors

Structure-from-motion and multiview-stereo together offer a computer vision technique for reconstructing detailed 3D models from overlapping images of anything from large landscapes to microscopic features. Because such models can be generated from ordinary photographs taken with standard cameras in ordinary lighting conditions, these techniques are revolutionising digital recording and analysis in archaeology and related subjects such as palaeontology, museum studies and art history. However, most published treatments so far have focused merely on this technique's ability to produce low-cost, high quality representations, with one or two also suggesting new opportunities for citizen science. However, perhaps the major artefact scale advantage comes from significantly enhanced possibilities for 3D morphometric analysis and comparative taxonomy. We wish to stimulate further discussion of this new research domain by considering a case study using a famous and contentious set of archaeological objects: the terracotta warriors of China's first emperor. © 2014 The Authors

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm

Three Dimensional Palmprint Recognition using Structured Light Imaging

BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems, Arlington, VA, 29-1 October 2008Palmprint is one of the most unique and stable biometric characteristics. Although 2D palmprint recognition can achieve high accuracy, the 2D palmprint images can be easily counterfeited and much 3D depth information is lost in the imaging process. This paper presents a new approach, 3D palmprint recognition, to exploit the 3D structural information of the palm surface. The structured-light imaging is used to acquire the 3D palmprint data, from which the features of Mean Curvature, Gauss Curvature and Surface Type (ST) are extracted. A fast feature matching and score level fusion strategy are then used to classify the input 3D palmprint data. With the established 3D palmprint database, a series of verification and identification experiments are conducted and the results show that 3D palmprint technique can achieve high recognition rate while having high anti-counterfeiting capability.Department of ComputingRefereed conference pape

Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation

The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.Comment: Accepted into ICCBS 202

Comparison of quasi-spherical surfaces : application to corneal biometry

In this study, the authors present two new techniques with their own particular advantages dedicated to the authentication of a person based on the three-dimensional geometry of the cornea. A device known as corneal topographer is used for capturing the shape of each cornea. Until now only a few studies on corneal biometry have been conducted and they were limited only to the anterior surface. In this study, since the whole cornea is a tissue layered by two (anterior and posterior) surfaces, the authors propose to use both surfaces to characterise the corneal shape. The first proposed method consists of comparing coefficients from a spherical harmonics decomposition, and this allows to do a fast comparison that can be used to perform many-to-one comparisons. The second approach is based on the minimal residual volume between two corneas after a registration step, this geometry-based method is more accurate but slower, and is thus used to perform one-to-one comparisons. A cascade fusion scheme is also proposed to benefit from the advantages of both methods. The authors’ study demonstrates that corneal shape could be used for biometry. The two proposed methods have been tested and validated on a dataset of 257 corneas

Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics

This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage

Human Ear recognition Using Geometric Features

Abstract: Biometrics is the study of automatic techniques for recognizing human beings based on physical or behavioral traits. To find good biometric features, technique has been researched extensively in recent years. Among several biometric features, ear is quite stable because it does not vary with age and emotion. The ear recognition work depends on ear height, reference line cut points, corresponding angles and inner ear curve. The study is performed on the ear in random orientation and shows a greater accuracy than existing dominant approach. The recognition accuracy is increased by using more training images for database. Face recognition by itself, using the same approach, gave a 63% rank one recognition rate, but when complimented with ear images in a multimodal system improved to 94% rank one recognition rate