INVESTIGADORES
RAMALLO Virginia
artículos
Título:
Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
Autor/es:
CINTAS CELIA ; QUINTO-SANCHEZ MIRSHA ; ACUÑA-ALONZO, VICTOR; PASCHETTA CAROLINA ; DE AZEVEDO SOLEDAD ; DE CERQUEIRA CAIO CESAR ; VIRGINIA RAMALLO; GALLO CARLA ; POLETTI GIOVANNI ; BORTOLINI MARIA CATIRA ; CANIZALES-QUINTEROS SAMUEL ; ROTHHAMMER FRANCISCO ; BEDOYA GABRIEL ; RUIZ-LINARES ANDRES ; GONZALEZ-JOSÉ ROLANDO ; DELRIEUX CLAUDIO
Revista:
IET Biometrics
Editorial:
Institution of Engineering and Technology
Referencias:
Año: 2016
ISSN:
2047-4938
Resumen:
Accurate gathering of phenotypic information is a key aspect in several subject matters, including biometric identification, biomedical analysis, forensics, and many other. Automatic identification of anatomical structures of biometric interest, such as fingerprints, iris patterns, or facial traits, are extensively used in applications like access control, anthropological research, and surveillance, all having in common the drawback of requiring intrusive means for acquiring the required information. In this regard, the ear structure has multiple advantages. Not only the ear?s biometric markers can be easily captured from the distance with non intrusive methods, but also they experiment almost no changes over time, and are not influenced by facial expressions. However, the most relevant proposals in the literature for automated ear detection do not take advantage of its phenotypic attributes that biologically determine the ear?s shape and form. Therefore, these methods require acquisition in controlled conditions to perform adequately. In this paper we present a new method, based on two well established methodologies, Geometric Morphometrics and Deep Learning algorithms, for automatic ear detection and feature extraction in the form of 2D landmarks. A convolutional neural network was trained with a set of manually landmarked examples. The trained network is able to provide morphometric landmarks on ears? images automatically, with a performance that matches human assisted landmarking. The ability to perform in the open (i.e., in images or video taken with no specific acquisition preparation), together with the feasibility of using ear landmarks as feature vectors for people identification, opens a novel spectrum of biometrics applications.