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 approach.

The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human ears, but also because of variable lighting conditions and the ever changing profile shape of an ear's projection when photographed. An ear detection system involving multiple convolutional neural networks and a detection grouping algorithm is proposed to identify the presence and location of an ear in a given input image. The proposed method matches the performance of other methods when analyzed against clean and purpose-shot photographs, reaching an accuracy of upwards of 98%, but clearly outperforms them with a rate of over 86% when the system is subjected to non-cooperative natural images where the subject appears in challenging orientations and photographic conditions.

Please cite this article in press as: P.L. Galdámez et al., A small look at the ear recognition process using a hybrid approach, Journal of Applied Logic (2015), http://dx. AbstractThe 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.

Abstract. This document provides an approach to biometrics analysis which consists in the location and identification of ears in real time. Ear features, which is a stable biometric approach that does not vary with age, have been used for many years in the forensic science of recognition. The ear has all the properties that a biometric trait should have, i.e. uniqueness, permanence, universality and collectability. Because it is a field of study with potential growth, in this paper, we summarize some of the approaches to the detection and recognition in existing 2D images in order to provide a perspective on the possible future research and the develop of a practical application of some of these methodologies to create finally a functional application for identification and recognition of individuals from an image of the ear, the above in the context of intelligent surveillance and criminal identification, one of the most important areas in the processes of identification.

The purpose of this paper is to offer an approach in the biometrics analysis field, using ears to recognize people. This study uses Hausdorff distance as a preprocessing stage adding sturdiness to increase the performance filtering for the subjects to use for testing stage of the neural network. Then, the system computes Speeded Up Robust Features (SURF) and Fisher Linear Discriminant Analysis (LDA) as an input of two neural networks to detect and recognize a person by the patterns of its ear. To show the applied theory in the experimental results; it also includes an application developed with Microsoft .net. The investigation which enhances the ear recognition process showed robustness through the integration of Hausdorff, LDA and SURF in neural networks.