3,766 research outputs found
A Survey on Ear Biometrics
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers
An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization
The human external ears, or pinnae, have an intriguing shape and, like most parts of the human external body, bilateral symmetry is observed between left and right. It is a well-known part of our auditory sensory system and mediates the spatial localization of incoming sounds in 3D from monaural cues due to its shape-specific filtering as well as binaural cues due to the paired bilateral locations of the left and right ears. Another less broadly appreciated aspect of the human pinna shape is its uniqueness from one individual to another, which is on the level of what is seen in fingerprints and facial features. This makes pinnae very useful in human identification, which is of great interest in biometrics and forensics. Anatomically, the type of symmetry observed is known as matching symmetry, with structures present as separate mirror copies on both sides of the body, and in this work we report the first such investigation of the human pinna in 3D. Within the framework of geometric morphometrics, we started by partitioning ear shape, represented in a spatially dense way, into patterns of symmetry and asymmetry, following a two-factor anova design. Matching symmetry was measured in all substructures of the pinna anatomy. However, substructures that stick out' such as the helix, tragus, and lobule also contained a fair degree of asymmetry. In contrast, substructures such as the conchae, antitragus, and antihelix expressed relatively stronger degrees of symmetric variation in relation to their levels of asymmetry. Insights gained from this study were injected into an accompanying identification setup exploiting matching symmetry where improved performance is demonstrated. Finally, possible implications of the results in the context of ear recognition as well as sound localization are discussed
Ear Contour Detection and Modeling Using Statistical Shape Models
Ear detection is an actively growing area of research because of its applications in human head tracking and biometric recognition. In head tracking, it is used to augment face detectors and to perform pose estimation. In biometric systems, it is used both as an independent modality and in multi-modal biometric recognition. The ear shape is the preferred feature used to perform detection because of its unique structure in both 2D color images and 3D range images. Ear shape models have also been used in literature to perform ear detection, but at a cost of a loss in information about the exact ear structure. In this thesis, we seek to address these issues in existing methods by a combination of techniques including Viola Jones Haar Cascades, Active Shape Models (ASM) and Dijkstra\u27s shortest path algorithm to devise a shape model of the ear using geometric parameters and mark an accurate contour around the ear using only 2D color images. The Viola Jones Haar Cascades classifier is used to mark a rectangular region around the ear in a left side profile image. Then a set of key landmark points around the ear including the ear outer helix, the ear anti-helix and the ear center is extracted using the ASM. This set of landmarks is then fed into Dijkstra\u27s shortest path algorithm which traces out the strongest edge between adjacent landmarks, to extract the entire ear outer contour, while maintaining a high computational efficiency
Anthropometric Individualization of Head-Related Transfer Functions Analysis and Modeling
Human sound localization helps to pay attention to spatially separated speakers using interaural level and time differences as well as angle-dependent monaural spectral cues. In a monophonic teleconference, for instance, it is much more difficult to distinguish between different speakers due to missing binaural cues. Spatial positioning of the speakers by means of binaural reproduction methods using head-related transfer functions (HRTFs) enhances speech comprehension. These HRTFs are influenced by the torso, head and ear geometry as they describe the propagation path of the sound from a source to the ear canal entrance. Through this geometry-dependency, the HRTF is directional and subject-dependent. To enable a sufficient reproduction, individual HRTFs should be used. However, it is tremendously difficult to measure these HRTFs. For this reason this thesis proposes approaches to adapt the HRTFs applying individual anthropometric dimensions of a user. Since localization at low frequencies is mainly influenced by the interaural time difference, two models to adapt this difference are developed and compared with existing models. Furthermore, two approaches to adapt the spectral cues at higher frequencies are studied, improved and compared. Although the localization performance with individualized HRTFs is slightly worse than with individual HRTFs, it is nevertheless still better than with non-individual HRTFs, taking into account the measurement effort
Iris Indexing and Ear Classification
To identify an individual using a biometric system, the input biometric data has to be typically compared against that of each and every identity in the existing database during the matching stage. The response time of the system increases with the increase in number of individuals (i.e., database size), which is not acceptable in real time monitoring or when working on large scale data. This thesis addresses the problem of reducing the number of database candidates to be considered during matching in the context of iris and ear recognition. In the case of iris, an indexing mechanism based on Burrows Wheeler Transform (BWT) is proposed. Experiments on the CASIA version 3 iris database show a significant reduction in both search time and search space, suggesting the potential of this scheme for indexing iris databases. The ear classification scheme proposed in the thesis is based on parameterizing the shape of the ear and assigning it to one of four classes: round, rectangle, oval and triangle. Experiments on the MAGNA database suggest the potential of this scheme for classifying ear databases
Large Deformation Diffeomorphic Metric Mapping Provides New Insights into the Link Between Human Ear Morphology and the Head-Related Transfer Functions
The research findings presented in this thesis is composed of four sections. In the first section of this thesis, it is shown how LDDMM can be applied to deforming head and ear shapes in the context of morphoacoustic study. Further, tools are developed to measure differences in 3D shapes using the framework of currents and also to compare and measure the differences between the acoustic responses obtained from BEM simulations for two ear shapes. Finally this section introduces the multi-scale approach for mapping ear shapes using LDDMM. The second section of the thesis estimates a template ear, head and torso shape from the shapes available in the SYMARE database. This part of the thesis explains a new procedure for developing the template ear shape. The template ear and head shapes were are verified by comparing the features in the template shapes to corresponding features in the CIPIC and SYMARE database population. The third section of the thesis examines the quality of the deformations from the template ear shape to target ears in SYMARE from both an acoustic and morphological standpoint. As a result of this investigation, it was identified that ear shapes can be studied more accurately by the use of two physical scales and that scales at which the ear shapes were studied were dependent on the parameters chosen when mapping ears in the LDDMM framework. Finally, this section concludes by noting how shape distances vary with the acoustic distances using the developed tools. In the final part of this thesis, the variations in the morphology of ears are examined using the Kernel Principle Component Analysis (KPCA) and the changes in the corresponding acoustics are studied using the standard principle component analysis (PCA). These examinations involved identifying the number of kernel principle components that are required in order to model ear shapes with an acceptable level of accuracy, both morphologically and acoustically
Human sound localisation cues and their relation to morphology
Binaural soundfield reproduction has the potential to create realistic threedimensional sound scenes using only a pair of normal headphones. Possible
applications for binaural audio abound in, for example, the music, mobile
communications and games industries. A problem exists, however, in that
the head-related transfer functions (HRTFs) which inform our spatial perception of sound are affected by variations in human morphology, particularly in the shape of the external ear. It has been observed that HRTFs
simply based on some kind of average head shape generally result in poor
elevation perception, weak externalisation and spectrally distorted sound
images. Hence, HRTFs are needed which accommodate these individual
differences. Direct acoustic measurement and acoustic simulations based
on morphological measurements are obvious means of obtaining individualised HRTFs, but both methods suffer from high cost and practical difficulties. The lack of a viable measurement method is currently hindering
the widespread adoption of binaural technologies. There have been many attempts to estimate individualised HTRFs effectively and cheaply using easily
obtainable morphological descriptors, but due to an inadequate understanding of the complex acoustic effects created in particular by the external ear,
success has been limited. The work presented in this thesis strengthens current understanding in several ways and provides a promising route towards
improved HRTF estimation. The way HRTFs vary as a function of direction is compared with localisation acuity to help pinpoint spectral features
which contribute to spatial perception. 50 subjects have been scanned using
magnetic resonance imaging to capture their head and pinna morphologies,
and HRTFs for the same group have been measured acoustically. To make
analysis of this extensive data tractable, and so reveal the mapping between
the morphological and acoustic domains, a parametric method for efficiently
describing head morphology has been developed. Finally, a novel technique,
referred to as morphoacoustic perturbation analysis (MPA), is described.
We demonstrate how MPA allows the morphological origin of a variety of
HRTF spectral features to be identified
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