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 article 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.

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A survey on ear biometrics

The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features. The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people. Owing to these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, the authors categorise and summarise approaches to ear detection and recognition in 2D and 3D images. Then, they provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which they consider to be the most important application of ear recognition characteristic in the near future.

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Ear biometrics: a survey of detection, feature extraction and recognition methods

Ear recognition is a new biometrics technique. Due to its unique physiological structure, position and stability, ear recognition is expected to be a promising authentication technique. In this paper, a hybrid system for classifying ear images is proposed. This system combines independent component analysis (ICA) and RBF network. The original ear image database is decomposed into linear combinations of several basic images. Then the corresponding coefficients of these combinations are fed up into RBF network instead of an original feature vector comprised of pixel values of grayscale images. The local features extraction of ICA and the adaptability of RBF neural network are combined reasonably. The robustness of the system is enhanced. The experiment results show that the recognition rate of ICA RBF method is improved substantially.

A novel approach for ear recognition based on ICA and RBF network

This paper presents the ear based authentication using Local Principal Independent Components (LPIC) an extension of PCA. As PCA is a global approach dealing with all pixel intensities, it is difficult to get finer details from the ear image. The concept of information sets is introduced in this paper so as to have leverage over the local information. These sets are based on the granularization of the ear image in the form of windows. The features based on these sets allow us to change the local information which goes into LPIC as the input. Thus LPIC not only uses this local information but also helps to reduce the dimensions of the deduced features far less than that can be achieved with PCA. For the extraction of sparse information from ear, features such as Effective information (EI), Energy feature (EF), Sigmoid feature (SF), Multi Quadratic feature (MQD) are derived and then LPIC is applied to get the reduced number of features. Inner product classifier (IPC) is developed for the classification of these features. The experiments carried out on constrained and unconstrained databases show that LPIC is effective not only under the ideal conditions but also under the unconstrained environment.

Robust ear based authentication using Local Principal Independent Components

En tanto basados en la captura metrica de caracteristicas anatomico-fisicas o comportamentales singularizantes y gestionados en forma que permiten un acceso casi inmediato a tal informacion, los sistemas de reconocimiento biometrico se nos presentan como una tecnologia perfecta. Pero existe un rango amplisimo de tecnologias biometricas, con muy diferentes niveles de calidad y eficiencia, y se emplean en un amplio abanico de escenarios y contextos a lo largo y ancho del Planeta. Tambien son diferentes las condiciones de procesamiento y almacenamiento de los datos, los niveles de interoperatividad o los riesgos para los derechos fundamentales de los titulares de los datos. Algunas afirmaciones que asocian, sin mas, Biometria y vigilancia perfecta se sostienen a veces sobre interpretaciones parciales de la verdad historica o cientifica, bien directamente sobre falacias y mitos y es importante analizar la realidad de los datos que manejan. 
El objetivo de la presente investigacion es abordar la metodologia, potencialidad y limitaciones de los sistemas de reconocimiento biometrico para, desde ese anclaje, dar respuesta a dos cuestiones: 
Primera, ?hasta que punto y como ofrecen las tecnologias de reconocimiento biometrico la posibilidad de identificar a un ser humano con unico en el mundo? 
Segunda, ?que razones estan permitiendo y cuales deberian impedir construir infraestructuras de reconocimiento biometrico que aseguren la identificacion en todo momento? 
La primera de las preguntas acepta la hipotesis cientifica de la que parten los sistemas de reconocimiento biometrico: la posibilidad de expresar metricamente (cifrar, cuantificar) la singularidad de algunas caracteristicas de las personas. Pero se adentra en validar hasta que punto el actual contexto cientifico y tecnologico es capaz de ofrecer y como sistemas de reconocimiento agiles y fiables basados en dicha hipotesis.

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H. Van Den Heuvel

Papers

Ear identification based on surveillance camera images.