TY - GEN
T1 - Estrategia para la detección de tipos de gestos faciales usando red neuronal SOM
AU - Raúl Eduardo, Huarote Zegarra
AU - Yensi, Vega Luján
AU - Edward José, Flores Masías
AU - Llanos Chacaltana, Katherine Susan
AU - Mónica, Díaz Reátegui
AU - Miguel Alfredo, Lévano Stella
N1 - Publisher Copyright:
© 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2022
Y1 - 2022
N2 - This research aims to cover a need to be able to classify gestures, specifically the gestures of people's faces, which reflects the emotions of each person such as anger, fear, happiness and sadness. To be able to identify these gestures, it is necessary to apply a strategy, which is to prepare the digital image matrices in a sequence, such as converting to gray tone, finding the orientation, applying the sobel and medfilt2 algorithm, so that this result can enter to a SOM neural network and be able to be classified according to the gestures. Labeling as 0 to anger, 1 to fear, 2 to happiness and 3 to sadness. To corroborate this strategy, a public database of faces has been taken, being 160 images of faces for the training and for the tests 15 images were used that were not part of the training and each image obtained in .jpg format in different dimensions, achieving demonstrate with this strategy an affectivity of 96.0% certainty in the identification of gestures.
AB - This research aims to cover a need to be able to classify gestures, specifically the gestures of people's faces, which reflects the emotions of each person such as anger, fear, happiness and sadness. To be able to identify these gestures, it is necessary to apply a strategy, which is to prepare the digital image matrices in a sequence, such as converting to gray tone, finding the orientation, applying the sobel and medfilt2 algorithm, so that this result can enter to a SOM neural network and be able to be classified according to the gestures. Labeling as 0 to anger, 1 to fear, 2 to happiness and 3 to sadness. To corroborate this strategy, a public database of faces has been taken, being 160 images of faces for the training and for the tests 15 images were used that were not part of the training and each image obtained in .jpg format in different dimensions, achieving demonstrate with this strategy an affectivity of 96.0% certainty in the identification of gestures.
KW - SOM neural network
KW - Strategy
KW - emotions
KW - gestures
UR - http://www.scopus.com/inward/record.url?scp=85150687384&partnerID=8YFLogxK
U2 - 10.18687/LEIRD2022.1.1.80
DO - 10.18687/LEIRD2022.1.1.80
M3 - Contribución a la conferencia
AN - SCOPUS:85150687384
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development
A2 - Larrondo Petrie, Maria M.
A2 - Texier, Jose
A2 - Matta, Rodolfo Andres Rivas
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development, LEIRD 2022
Y2 - 6 December 2022 through 7 December 2022
ER -