The effect of time on ear biometrics

We present an experimental study to demonstrate the effect of the time difference in image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the time effect on ear biometrics. For the purpose of recognition, we convolve banana wavelets with an ear image and then apply local binary pattern on the convolved image. The histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the histograms of two ears to measure the ear similarity for the recognition purposes. We also use analysis of variance (ANOVA) to select features to identify the best banana wavelets for the recognition process. The experimental results show that the recognition rate is only slightly reduced by time. The average recognition rate of 98.5% is achieved for an eleven month-difference between gallery and probe on an un-occluded ear dataset of 1491 images of ears selected from Southampton University ear database

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

Computer Aided Multi-Data Fusion Dismount Modeling

Recent research efforts strive to address the growing need for dismount surveillance, dismount tracking and characterization. Current work in this area utilizes hyperspectral and multispectral imaging systems to exploit spectral properties in order to detect areas of exposed skin and clothing characteristics. Because of the large bandwidth and high resolution, hyperspectral imaging systems pose great ability to characterize and detect dismounts. A multi-data dismount modeling system where the development and manipulation of dismount models is a necessity. This thesis demonstrates a computer aided multi-data fused dismount model, which facilitates studies of dismount detection, characterization and identification. The system is created by fusing: pixel mapping, signature attachment, and pixel mixing algorithms. The developed multi-data dismount model produces simulated hyperspectral images that closely represent an image collected by a hyperspectral imager. The dismount model can be modified to fit the researcher\u27s needs. The multi-data model structure allows the employment of a database of signatures acquired from several sources. The model is flexible enough to allow further exploitation, enhancement and manipulation. The multi-data dismount model developed in this effort fulfills the need for a dismount modeling tool in a hyperspectral imaging environment

Finger Movements Based on Biometric Authentication for Touch Devices

The primary goal of this thesis is to collect and compare the touch parameters of finger movements on touch devices and build personal profile with good-recognition parameters to indicate the touch characteristics of individual users using the touch devices. In order to study the possibility of implementation of touch-style identification for touch devices, this work mainly focuses on finding and testing the possible touch parameters which could be used to compose a profile to verify the users. A full test with an developed anroid application on tablet was performed by 20 subjects to collect touch information, including location of finger points, finger pressure force and speed of finger movements. Statistical analysis was applied on each dataset of the users. The finding has shown that each user can be identified by the discriminative information of finger movements on the touch screen. The results show huge difference in mean, standard deviation and skewness for the dataset of each user giving a reason to hope the implementation of finger movements based on biometric authentication for touch devices. Hopefully, the result of this project will be valuable for further research of implementation of biometric authentication on touch devices based on the finger movements

Estudio de verificación biométrica de voz

En este proyecto estudia la posibilidad de realizar una verificación de locutor por medio de la biometría de voz. En primer lugar se obtendrán las características principales de la voz, que serían los coeficientes MFCC, partiendo de una base de datos de diferentes locutores con 10 muestras por cada locutor. Con estos resultados se procederá a la creación de los clasificadores con los que luego testearemos y haremos la verificación. Como resultado final obtendremos un sistema capaz de identificar si el locutor es el que buscamos o no. Para la verificación se utilizan clasificadores Support Vector Machine (SVM), especializado en resolver problemas biclase. Los resultados demuestran que el sistema es capaz de verificar que un locutor es quien dice ser comparándolo con el resto de locutores disponibles en la base de datos. ABSTRACT. Verification based on voice features is an important task for a wide variety of applications concerning biometric verification systems. In this work, we propose a human verification though the use of their voice features focused on supervised training classification algorithms. To this aim we have developed a voice feature extraction system based on MFCC features. For classification purposed we have focused our work in using a Support Vector Machine classificator due to it’s optimization for biclass problems. We test our system in a dataset composed of various individuals of di↵erent gender to evaluate our system’s performance. Experimental results reveal that the proposed system is capable of verificating one individual against the rest of the dataset

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

CONTACTLESS FINGERPRINT BIOMETRICS: ACQUISITION, PROCESSING, AND PRIVACY PROTECTION

Biometrics is defined by the International Organization for Standardization (ISO) as \u201cthe automated recognition of individuals based on their behavioral and biological characteristics\u201d Examples of distinctive features evaluated by biometrics, called biometric traits, are behavioral characteristics like the signature, gait, voice, and keystroke, and biological characteristics like the fingerprint, face, iris, retina, hand geometry, palmprint, ear, and DNA. The biometric recognition is the process that permits to establish the identity of a person, and can be performed in two modalities: verification, and identification. The verification modality evaluates if the identity declared by an individual corresponds to the acquired biometric data. Differently, in the identification modality, the recognition application has to determine a person's identity by comparing the acquired biometric data with the information related to a set of individuals. Compared with traditional techniques used to establish the identity of a person, biometrics offers a greater confidence level that the authenticated individual is not impersonated by someone else. Traditional techniques, in fact, are based on surrogate representations of the identity, like tokens, smart cards, and passwords, which can easily be stolen or copied with respect to biometric traits. This characteristic permitted a wide diffusion of biometrics in different scenarios, like physical access control, government applications, forensic applications, logical access control to data, networks, and services. Most of the biometric applications, also called biometric systems, require performing the acquisition process in a highly controlled and cooperative manner. In order to obtain good quality biometric samples, the acquisition procedures of these systems need that the users perform deliberate actions, assume determinate poses, and stay still for a time period. Limitations regarding the applicative scenarios can also be present, for example the necessity of specific light and environmental conditions. Examples of biometric technologies that traditionally require constrained acquisitions are based on the face, iris, fingerprint, and hand characteristics. Traditional face recognition systems need that the users take a neutral pose, and stay still for a time period. Moreover, the acquisitions are based on a frontal camera and performed in controlled light conditions. Iris acquisitions are usually performed at a distance of less than 30 cm from the camera, and require that the user assume a defined pose and stay still watching the camera. Moreover they use near infrared illumination techniques, which can be perceived as dangerous for the health. Fingerprint recognition systems and systems based on the hand characteristics require that the users touch the sensor surface applying a proper and uniform pressure. The contact with the sensor is often perceived as unhygienic and/or associated to a police procedure. This kind of constrained acquisition techniques can drastically reduce the usability and social acceptance of biometric technologies, therefore decreasing the number of possible applicative contexts in which biometric systems could be used. In traditional fingerprint recognition systems, the usability and user acceptance are not the only negative aspects of the used acquisition procedures since the contact of the finger with the sensor platen introduces a security lack due to the release of a latent fingerprint on the touched surface, the presence of dirt on the surface of the finger can reduce the accuracy of the recognition process, and different pressures applied to the sensor platen can introduce non-linear distortions and low-contrast regions in the captured samples. Other crucial aspects that influence the social acceptance of biometric systems are associated to the privacy and the risks related to misuses of biometric information acquired, stored and transmitted by the systems. One of the most important perceived risks is related to the fact that the persons consider the acquisition of biometric traits as an exact permanent filing of their activities and behaviors, and the idea that the biometric systems can guarantee recognition accuracy equal to 100\% is very common. Other perceived risks consist in the use of the collected biometric data for malicious purposes, for tracing all the activities of the individuals, or for operating proscription lists. In order to increase the usability and the social acceptance of biometric systems, researchers are studying less-constrained biometric recognition techniques based on different biometric traits, for example, face recognition systems in surveillance applications, iris recognition techniques based on images captured at a great distance and on the move, and contactless technologies based on the fingerprint and hand characteristics. Other recent studies aim to reduce the real and perceived privacy risks, and consequently increase the social acceptance of biometric technologies. In this context, many studies regard methods that perform the identity comparison in the encrypted domain in order to prevent possible thefts and misuses of biometric data. The objective of this thesis is to research approaches able to increase the usability and social acceptance of biometric systems by performing less-constrained and highly accurate biometric recognitions in a privacy compliant manner. In particular, approaches designed for high security contexts are studied in order improve the existing technologies adopted in border controls, investigative, and governmental applications. Approaches based on low cost hardware configurations are also researched with the aim of increasing the number of possible applicative scenarios of biometric systems. The privacy compliancy is considered as a crucial aspect in all the studied applications. Fingerprint is specifically considered in this thesis, since this biometric trait is characterized by high distinctivity and durability, is the most diffused trait in the literature, and is adopted in a wide range of applicative contexts. The studied contactless biometric systems are based on one or more CCD cameras, can use two-dimensional or three-dimensional samples, and include privacy protection methods. The main goal of these systems is to perform accurate and privacy compliant recognitions in less-constrained applicative contexts with respect to traditional fingerprint biometric systems. Other important goals are the use of a wider fingerprint area with respect to traditional techniques, compatibility with the existing databases, usability, social acceptance, and scalability. The main contribution of this thesis consists in the realization of novel biometric systems based on contactless fingerprint acquisitions. In particular, different techniques for every step of the recognition process based on two-dimensional and three-dimensional samples have been researched. Novel techniques for the privacy protection of fingerprint data have also been designed. The studied approaches are multidisciplinary since their design and realization involved optical acquisition systems, multiple view geometry, image processing, pattern recognition, computational intelligence, statistics, and cryptography. The implemented biometric systems and algorithms have been applied to different biometric datasets describing a heterogeneous set of applicative scenarios. Results proved the feasibility of the studied approaches. In particular, the realized contactless biometric systems have been compared with traditional fingerprint recognition systems, obtaining positive results in terms of accuracy, usability, user acceptability, scalability, and security. Moreover, the developed techniques for the privacy protection of fingerprint biometric systems showed satisfactory performances in terms of security, accuracy, speed, and memory usage

Human ear recognition by computer

Biometrics deals with recognition of individuals based on their physiological or behavioral characteristics. The human ear is a new feature in biometrics that has several merits over the more common face, fingerprint and iris biometrics. Unlike the fingerprint and iris, it can be easily captured from a distance without a fully cooperative subject, although sometimes it may be hidden with hair, scarf and jewellery. Also, unlike a face, the ear is a relatively stable structure that does not change much with the age and facial expressions. ""Human Ear Recognition by Computer"" is the first book