A Small Look at the Ear Recognition Process using a Hybrid Approach

The purpose of this document is to offer a combined approach in biometric analysis field, integrating some of the most known techniques using ears to recognize people. This study uses Hausdorff distance as a pre-processing stage adding sturdiness to increase the performance filtering for the subjects to use it in the testing process. Also includes the Image Ray Transform (IRT) and the Haar based classifier for the detection step. Then, the system computes Speeded Up Robust Features (SURF) and Linear Discriminant Analysis (LDA) as an input of two neural networks to recognize a person by the patterns of its ear. To show the applied theory experimental results, the above algorithms have been implemented using Microsoft C#. The investigation results showed robustness improving the ear recognition process

A Brief Review of the Ear Recognition Process using Deep Neural Networks

The process of precisely recognize people by ears has been getting major attention in recent years. It represents an important step in the biometric research, especially as a complement to face recognition systems which have difficult in real conditions. This is due to the great variation in shapes, variable lighting conditions, and the changing profile shape which is a planar representation of a complex object. An ear recognition system involving a convolutional neural networks (CNN) is proposed to identify a person given an input image. The proposed method matches the performance of other traditional approaches when analyzed against clean photographs. However, the F1 metric of the results shows improvements in specificity of the recognition. We also present a technique for improving the speed of a CNN applied to large input images through the optimization of the sliding window approac

A Brief Approach to the Ear Recognition Process

This paper offers an approach to biometric analysis using ears for recognition. The ear has all the assets that a biometric trait should possess. Because it is a study field in potential growth, this study offers an approach using SURF features as an input of a neural network with the purpose to detect and recognize a person by the patterns of its ear, also includes, the development of an application with .net to show experimental results of the theory applied. Ear characteristics, which are a unchanging biometric approach that does not vary with age, have been used for several years in the forensic science of recognition, thats why the research gets important value in the present. To perform this study, we worked with the help of Police School of Ávila, Province of Spain, we have built a database with approximately 300 ears

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Ear Biometrics: A Comprehensive Study of Taxonomy, Detection, and Recognition Methods

Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice to identify individuals in controlled or challenging environments. The outer part of the ear demonstrates high discriminative information across individuals and has shown to be robust for recognition. In addition, the data acquisition procedure is contactless, non-intrusive, and covert. This work focuses on using ear images for human authentication in visible and thermal spectrums. We perform a systematic study of the ear features and propose a taxonomy for them. Also, we investigate the parts of the head side view that provides distinctive identity cues. Following, we study the different modules of the ear recognition system. First, we propose an ear detection system that uses deep learning models. Second, we compare machine learning methods to state traditional systems\u27 baseline ear recognition performance. Third, we explore convolutional neural networks for ear recognition and the optimum learning process setting. Fourth, we systematically evaluate the performance in the presence of pose variation or various image artifacts, which commonly occur in real-life recognition applications, to identify the robustness of the proposed ear recognition models. Additionally, we design an efficient ear image quality assessment tool to guide the ear recognition system. Finally, we extend our work for ear recognition in the long-wave infrared domains

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Sistema biométrico para detección y reconocimiento de orejas basado en algoritmos de procesamiento de imágenes y redes neuronales profundas

[EN]The ear is an emerging biometric feature that has caught the attention of the scientific community for more than a decade. Its unique structure has stood out since long ago among forensic scientists, and has been used to identify suspects in many cases. The logical step towards a broader application of ear biometrics is to create a recognition system. To carry out this process, this work focuses on the use of data from images (2D). The present study mentions techniques like the Hausdorff distance, which adds robustness and increases the performance, filtering the subjects to use in the testing process. It also includes image ray transform (IRT) in the detection step. The ear is a fickle biometric feature when working with photographic images under varying conditions. This is largely due to the camera’s focus, the irregular shapes of the captures, the lighting conditions and the ever-changing shape of the projection when it is photographed. Therefore, to identify the presence and location of an ear in an image, we propose an ear detection system with multiple convolutional neural networks (CNN) and a clustering algorithm of detections. The proposed method coincides with the performance of other techniques when we analyze clean photographs, that is to say, catches in ideal conditions (purposeshot), reaching an accuracy of more than 98 %. When the system is subjected to natural images in real world conditions, where the subject appears in a multitude of orientations and photographic conditions in an uncontrolled environment, our system maintains the same precision, clearly exceeding the average result (83 %) obtained in previous researches. Finally, the algorithms used to complete the recognition steps are presented, using convolutional structures, extraction techniques and geometric approximations in order to increase the accuracy of the process.[ES]La oreja es un rasgo biométrico emergente que ha llamado la atención de la comunidad científica por más de una década. Su estructura única ha destacado desde hace mucho tiempo entre los científicos forenses, y se ha utilizado para la identificación de sospechosos en muchos casos. El paso lógico hacia una aplicación más amplia de la biometría de orejas es crear un sistema de reconocimiento. Este trabajo se centra en el uso de datos de imágenes (2D) para llevar a cabo dicho proceso. El presente estudio aborda técnicas como la distancia Hausdorff; la cual agrega robustez e incrementa el desempeño filtrando los sujetos a utilizar en la etapa de prueba del proceso. También incluye la transformación de imágenes con rayos (IRT) en la etapa de detección. La oreja es una característica biométrica inconstante cuando se trabaja con imágenes fotográficas en condiciones variables: esto se debe en gran parte al enfoque de la cámara, las formas irregulares de las capturas, las condiciones de iluminación y la forma siempre cambiante de la proyección cuando es fotografiada. Por tanto, para identificar la presencia y localización de una oreja en una imagen proponemos un sistema de detección de orejas con múltiples redes neuronales convolucionales (CNN) y un algoritmo de agrupación de detección. El método propuesto coincide con el rendimiento de otras técnicas cuando analizamos fotografías limpias, es decir, capturas en condiciones ideales (purposeshot), alcanzando una precisión de más del 98 %. Cuando el sistema está sujeto a imágenes naturales en condiciones del mundo real, donde el sujeto aparece en una multitud de orientaciones y condiciones fotográficas en ambiente no controlado, nuestro sistema mantiene la misma precisión superando claramente el resultado del 83 % promedio obtenido en investigaciones previas. Finalmente se exponen los algoritmos utilizados para completar los pasos del reconocimiento, utilizando estructuras convolucionales, técnicas de extracción de características y aproximaciones geométricas a fin de incrementar la presición del proceso

Combining shape and color. A bottom-up approach to evaluate object similarities

The objective of the present work is to develop a bottom-up approach to estimate the similarity between two unknown objects. Given a set of digital images, we want to identify the main objects and to determine whether they are similar or not. In the last decades many object recognition and classification strategies, driven by higher-level activities, have been successfully developed. The peculiarity of this work, instead, is the attempt to work without any training phase nor a priori knowledge about the objects or their context. Indeed, if we suppose to be in an unstructured and completely unknown environment, usually we have to deal with novel objects never seen before; under these hypothesis, it would be very useful to define some kind of similarity among the instances under analysis (even if we do not know which category they belong to). To obtain this result, we start observing that human beings use a lot of information and analyze very different aspects to achieve object recognition: shape, position, color and so on. Hence we try to reproduce part of this process, combining different methodologies (each working on a specific characteristic) to obtain a more meaningful idea of similarity. Mainly inspired by the human conception of representation, we identify two main characteristics and we called them the implicit and explicit models. The term "explicit" is used to account for the main traits of what, in the human representation, connotes a principal source of information regarding a category, a sort of a visual synecdoche (corresponding to the shape); the term "implicit", on the other hand, accounts for the object rendered by shadows and lights, colors and volumetric impression, a sort of a visual metonymy (corresponding to the chromatic characteristics). During the work, we had to face several problems and we tried to define specific solutions. In particular, our contributions are about: - defining a bottom-up approach for image segmentation (which does not rely on any a priori knowledge); - combining different features to evaluate objects similarity (particularly focusiing on shape and color); - defining a generic distance (similarity) measure between objects (without any attempt to identify the possible category they belong to); - analyzing the consequences of using the number of modes as an estimation of the number of mixture’s components (in the Expectation-Maximization algorithm)

Discrete Optimization Methods for Segmentation and Matching

This dissertation studies discrete optimization methods for several computer vision problems. In the first part, a new objective function for superpixel segmentation is proposed. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. I present a new graph construction for images and show that this construction induces a matroid. The segmentation is then given by the graph topology which maximizes the objective function under the matroid constraint. By exploiting submodular and monotonic properties of the objective function, I develop an efficient algorithm with a worst-case performance bound of $\frac{1}{2}$ for the superpixel segmentation problem. Extensive experiments on the Berkeley segmentation benchmark show the proposed algorithm outperforms the state of the art in all the standard evaluation metrics. Next, I propose a video segmentation algorithm by maximizing a submodular objective function subject to a matroid constraint. This function is similar to the standard energy function in computer vision with unary terms, pairwise terms from the Potts model, and a novel higher-order term based on appearance histograms. I show that the standard Potts model prior, which becomes non-submodular for multi-label problems, still induces a submodular function in a maximization framework. A new higher-order prior further enforces consistency in the appearance histograms both spatially and temporally across the video. The matroid constraint leads to a simple algorithm with a performance bound of $\frac{1}{2}$. A branch and bound procedure is also presented to improve the solution computed by the algorithm. The last part of the dissertation studies the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem, chamfer matching remains to be the preferred method when speed and robustness are considered. In this dissertation, I significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). It is achieved by incorporating edge orientation information in the matching algorithm so the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, I present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows one to bound the cost distribution of large neighborhoods and skip the bad hypotheses. Experiments show that the proposed approach improves the speed of the original chamfer matching up to an order of 45 times, and it is much faster than many state of art techniques while the accuracy is comparable. I further demonstrate the application of the proposed algorithm in providing seamless operation for a robotic bin picking system