Automatic ear detection and feature extraction using Geometric Morphometrics and convolutional neural networks. Issue 3 (31st January 2017)

• Record Type: Journal Article
• Authors: Cintas, Celia; Quinto‐Sánchez, Mirsha; Acuña, Victor; Paschetta, Carolina; de Azevedo, Soledad; Cesar Silva de Cerqueira, Caio; Ramallo, Virginia; Gallo, Carla; Poletti, Giovanni; Bortolini, Maria Catira; Canizales‐Quinteros, Samuel; Rothhammer, Francisco; Bedoya, Gabriel; Ruiz‐Linares, Andres; Gonzalez‐José, Rolando; Delrieux, Claudio
• Is Part Of: IET biometrics. Volume 6:Issue 3(2017)
• Page Start: 211
• Page End: 223
• Publication Date: 2017-01-31
• Subjects: learning (artificial intelligence) -- neural nets -- feature extraction -- computational geometry -- image matching -- biometrics (access control); feature extraction -- geometric morphometrics -- phenotypic information -- anatomical structure identification -- fingerprints -- iris patterns -- facial traits -- ear structure -- ear biometric markers -- nonintrusive method -- facial expressions -- phenotypic attributes -- deep‐learning algorithms -- automatic ear detection -- 2D landmarks -- convolutional neural network training -- morphometric landmarks -- human‐assisted landmark matching -- feature vectors -- people identification; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2016.0002
• Languages: English
• ISSNs: 2047-4938

Abstract

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.