Method for Human Ear Localization in Controlled and Uncontrolled Environments

Eydi Lopez-Hernandez, Andrea Magadan-Salazar (), Raúl Pinto-Elías, Nimrod González-Franco and Miguel A. Zuniga-Garcia
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Eydi Lopez-Hernandez: Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Morelos, Mexico
Andrea Magadan-Salazar: Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Morelos, Mexico
Raúl Pinto-Elías: Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Morelos, Mexico
Nimrod González-Franco: Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Morelos, Mexico
Miguel A. Zuniga-Garcia: PCI Energy Solutions, Norman, OK 73072, USA

Mathematics, 2024, vol. 12, issue 7, 1-24

Abstract: One of the fundamental stages in recognizing people by their ears, which most works omit, is locating the area of interest. The sets of images used for experiments generally contain only the ear, which is not appropriate for application in a real environment, where the visual field may contain part of or the entire face, a human body, or objects other than the ear. Therefore, determining the exact area where the ear is located is complicated, mainly in uncontrolled environments. This paper proposes a method for ear localization in controlled and uncontrolled environments using MediaPipe, a tool for face localization, and YOLOv5s architecture for detecting the ear. The proposed method first determines whether there are cues that indicate that a face exists in an image, and then, using the MediaPipe facial mesh, the points where an ear potentially exists are obtained. The extracted points are employed to determine the ear length based on the proportions of the human body proposed by Leonardo Da Vinci. Once the dimensions of the ear are obtained, the delimitation of the area of interest is carried out. If the required elements are not found, the model uses the YOLOv5s architecture module, trained to recognize ears in controlled environments. We employed four datasets for testing (i) In-the-wild Ear Database, (ii) IIT Delhi Ear Database, (iii) AMI Ear Database, and (iv) EarVN1.0. Also, we used images from the Internet and some acquired using a Redmi Note 11 cell phone camera. An accuracy of 97% with an error of 3% was obtained with the proposed method, which is a competitive measure considering that tests were conducted in controlled and uncontrolled environments, unlike state-of-the-art methods.

Keywords: localization of the area of interest; ear biometric system; MediaPipe; YOLOv5s (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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