It can be easily captured from a distance without a fully cooperative subject. Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. In addition, the statistical analysis shows that there is an important difference between the left and right ears of the same person; hence both ears can be treated as a respective class in 3D ear recognition. This Chapter presents a novel method of 3D ear acquisition system and 3D ear coordinate direction normalization based on projection density. After recognizing each single modality which was the fingerprint, the face as well as the voice, we recovered three similarity scores. This information is provided by a service that aggregates data from review sources and other sources that are often consulted by libraries, and readers.
The University does not edit this information and merely includes it as a convenience for users. These characteristics cannot be extracted from 2D ear images. Experimental results demonstrate that the identity established by such an integrated system is more reliable than the established identity by fingerprint recognition system, facial verification system and a voice verification system. The experimental results show that the ear-cheek angle is unique and stable for each person and distinguishable for different people, which is a useful feature in 3D ear classification and indexing. Compared with the original results, all three methods yielded better results as expected. The last part presents generic chapters on multi-biometrics fusion methodologies and performance prediction techniques. To this end, at first, we propose a template-based ear detection method.
Considering both the speed of convergence and robustness to image degradations induced by high system magnifications and long observation distances, we introduce an auto-focusing algorithm based on sequential search with a variable step size. For this approach, classification methods have been the basis of important recognition accuracy improvements. Fusion phases are performed at score level. The process of score normalization consists of changing the scale parameters of the underlying match score distributions to ensure compatibility between multiple score types. Also, the ear has a relatively stable structure that does not change much with the age and facial expressions. Generally, a biometric authentication system functions by capturing the biometric trait of a person and comparing it with previously-acquired compatible data which is typically in the form of a person-specific template. Gait recognition research is still at its infancy, however, and we have yet to solve the fundamental issue of finding gait features which at once have sufficient discrimination power and can be extracted robustly and accurately from low-resolution video.
Emphasis is placed on biometric systems where single sensor and single modality are employed in challenging imaging conditions. The problem is that once you have gotten your nifty new product, the face biometrics for personal identification abidi besma abidi mongi a gets a brief glance, maybe a once over, but it often tends to get discarded or lost with the original packaging. Deteriorations unique to high magnification and long range face images are investigated. Biometric authentication has four major steps: a capture of the raw data of the biometric trait; b feature extraction, which processes the raw data from the previous step to extract features that are a condensed representation of the trait; c feature matching, which utilizes a classifier to compare the extracted features with the templates recorded in the database; and d finally, the decision step, which utilizes the matching scores to either grant or deny access to the authenticating user. More recently, the need for such capabilities has extended to long range surveillance and wide area monitoring such as forest fires, harbors, and waterways.
With respect to the ear identification, we resort to the l1-minimization based sparse representation. In this paper, we present a number of sensor arrangements for such applications, focusing on optical setups, auto-focusing mechanisms, and image deblurring techniques. In this paper, we present a novel method of 3D ear acquisition system by using triangulation imaging principle, and the experiment results show that this design is efficient and can be used for ear recognition. The corresponding system magnification is elevated from less than 3 × to 20 × for indoor and up to 375 × for outdoor. This practical reference contains four distinctive parts and a brief introduction chapter. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases.
The last part presents generic chapters on multi-biometrics fusion methodologies and performance prediction techniques. Most existing face related research is restricted to close range applications with low and constant system magnifications camera zoom. The human ear is a new feature in biometrics that has several merits over the more common face, fingerprint and iris biometrics. Labeling of connected components is one of the most fundamental operations in the area of image and video processing. It does not warrant that reviews are accurate. While this book covers a range of biometric traits including facial geometry, 3D ear form, fingerprints, vein structure, voice, and gait, its main emphasis is placed on multi-sensory and multi-modal face biometrics algorithms and systems. This paper presents a simulation study for a fusion or combiner algorithm for an ensemble classifier, which facilitates multi-modal biometric user authentication.
The second part on multi-sensory face biometrics deals with the personal identification task in challenging variable illuminations and outdoor operating scenarios by employing visible and thermal sensors. This paper describes a novel gait recognition technique based on the image self-similarity of a walking person. This fusion technique is only dependent on the score generated from each biometric module. For each 3D ear, a feature vector can be generated as its representation. Magnification blur proves to be a major degradation source for face recognition and is addressed via blur assessment and deblurring algorithms. To overcome this disadvantage, two methods are proposed: geometry mapping based on a polynomial imaging model and closed loop cooperative tracking.
Also, unlike a face, the ear has a relatively stable structure that does not change much with the age and facial expressions. By utilizing such a method, the extracted ear regions are represented in a common standard coordinate system determined by the template, which facilitates the following feature extraction and classification. The effectiveness of the proposed algorithms is illustrated via experiments and comparisons with existing systems. It has been written for a professional audience of both researchers and practitioners within industry, and is also ideal as an informative text for graduate students in computer science and engineering. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers' temporal and spatial resolution are still the foremost considerations in eye movement biometrics. The human ear is a new feature in biometrics that has several merits over the more common face, fingerprint and iris.