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Automatic ear detection and feature extraction using Geometric Morphometrics and convolutional neural networks

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dc.contributor.author Cintas, C.
dc.contributor.author Quinto-Sanchez, M.
dc.contributor.author Acuna, V.
dc.contributor.author Paschetta, C.
dc.contributor.author de Azevedo, S.
dc.contributor.author de Cerqueira, C. C. S.
dc.contributor.author Ramallo, V.
dc.contributor.author Gallo López-Aliaga, Carla Maria
dc.contributor.author Poletti, Giovanni
dc.contributor.author Bortolini, M. C.
dc.contributor.author Canizales-Quinteros, S.
dc.contributor.author Rothhammer, F.
dc.contributor.author Bedoya, G.
dc.contributor.author Ruiz-Linares, A.
dc.contributor.author Gonzalez-Jose, R.
dc.contributor.author Delrieux, C.
dc.date.accessioned 2019-01-25T16:36:28Z
dc.date.available 2019-01-25T16:36:28Z
dc.date.issued 2016
dc.identifier.uri https://hdl.handle.net/20.500.12866/4833
dc.description.abstract Accurate gathering of phenotypic information is a key aspect in several subject matters, including biometrics, 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 and anthropological research, 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. Here we present a new method based on Geometric Morphometrics and Deep Learning for automatic ear detection and feature extraction in the form of landmarks. A convolutional neural network was trained with a set of manually landmarked examples. The network is able to provide morphometric landmarks on ears' images automatically, with a performance that matches human landmarking. The feasibility of using ear landmarks as feature vectors opens a novel spectrum of biometrics applications. en_US
dc.language.iso eng
dc.publisher Wiley
dc.relation.ispartofseries IET biometrics
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject identification en_US
dc.subject model en_US
dc.subject 2D landmarks en_US
dc.subject anatomical structure identification en_US
dc.subject automatic ear detection en_US
dc.subject biometrics (access control) en_US
dc.subject computational geometry en_US
dc.subject Computer Science en_US
dc.subject convolutional neural network en_US
dc.subject deep-learning algorithms en_US
dc.subject ear biometric markers en_US
dc.subject ear structure en_US
dc.subject facial en_US
dc.subject facial expressions en_US
dc.subject feature extraction en_US
dc.subject feature vectors en_US
dc.subject fingerprints en_US
dc.subject geometric morphometrics en_US
dc.subject human-assisted landmark matching en_US
dc.subject image matching en_US
dc.subject iris patterns en_US
dc.subject learning (artificial intelligence) en_US
dc.subject morphometric landmarks en_US
dc.subject neural nets en_US
dc.subject nonintrusive method en_US
dc.subject pattern en_US
dc.subject people identification en_US
dc.subject phenotypic attributes en_US
dc.subject phenotypic information en_US
dc.subject position en_US
dc.subject recognition en_US
dc.subject shape en_US
dc.subject training en_US
dc.subject traits en_US
dc.title Automatic ear detection and feature extraction using Geometric Morphometrics and convolutional neural networks en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1049/iet-bmt.2016.0002
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#1.02.01
dc.relation.issn 2047-4946


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