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Open AccessArticle

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

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CICATA Unidad Legaria, Instituto Politécnico Nacional, Av. Legaria No. 694 Col. Irrigación, CDMX 11500 Mexico City, México
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CIITEC, Instituto Politécnico Nacional, Cerrada Cecati s/n Col. Sta. Catarina, Azc., CDMX 02250 Mexico City, México
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CIC, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, CDMX 07738 Mexico City, México
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CIDETEC, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, CDMX 07700 Mexico City, México
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Authors to whom correspondence should be addressed.
Sensors 2019, 19(3), 578; https://doi.org/10.3390/s19030578
Received: 16 November 2018 / Revised: 30 December 2018 / Accepted: 10 January 2019 / Published: 30 January 2019
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system. View Full-Text
Keywords: face recognition; assistive technologies; facial expressions; impaired vision; alpha-beta associative memories; correlation matrix; k-nearest neighbors (KNN); associative memory face recognition; assistive technologies; facial expressions; impaired vision; alpha-beta associative memories; correlation matrix; k-nearest neighbors (KNN); associative memory
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Márquez-Olivera, M.; Juárez-Gracia, A.-G.; Hernández-Herrera, V.; Argüelles-Cruz, A.-J.; López-Yáñez, I. System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia. Sensors 2019, 19, 578.

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