TY - GEN
T1 - A Blink Detection Algorithm Based on Image Processing and Convolutional Neural Networks
AU - Avalos, Mariel
AU - Binasco, Salvatore
AU - Kemper, Guillermo
AU - Salazar-Gamarra, Rodrigo
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Blink detection is an important task for human-computer interaction and behavior analysis. Although there is previous research regarding drowsiness detection, computer vision syndrome, and computer access by disabled patients, these have certain limitations for their algorithm’s accuracy due to a wide range of acquisition. Particularly, head movements, scene conditions, and the number of people in a frame present the main limiting factors. This paper proposes a low latency algorithm based on image processing and a convolutional neural network (CNN). The first technique is used to simplify the amount of computational cost by reducing the input data of the CNN. Then, the CNN is used to classify whether a specific frame is in an ‘open’ or ‘closed’ eye state. As this proposal was tested in a development board, limited CPU specifications and a reduced image database were considered for the CNN architecture and its training. The algorithm was tested using a CSI camera and a Jetson Nano 4 GB development board, obtaining a 99.5% accuracy for blink detection.
AB - Blink detection is an important task for human-computer interaction and behavior analysis. Although there is previous research regarding drowsiness detection, computer vision syndrome, and computer access by disabled patients, these have certain limitations for their algorithm’s accuracy due to a wide range of acquisition. Particularly, head movements, scene conditions, and the number of people in a frame present the main limiting factors. This paper proposes a low latency algorithm based on image processing and a convolutional neural network (CNN). The first technique is used to simplify the amount of computational cost by reducing the input data of the CNN. Then, the CNN is used to classify whether a specific frame is in an ‘open’ or ‘closed’ eye state. As this proposal was tested in a development board, limited CPU specifications and a reduced image database were considered for the CNN architecture and its training. The algorithm was tested using a CSI camera and a Jetson Nano 4 GB development board, obtaining a 99.5% accuracy for blink detection.
KW - Blink detection
KW - Convolutional neural network
KW - Image processing
UR - https://www.scopus.com/pages/publications/85135009767
U2 - 10.1007/978-3-031-08545-1_60
DO - 10.1007/978-3-031-08545-1_60
M3 - Contribución a la conferencia
AN - SCOPUS:85135009767
SN - 9783031085444
T3 - Smart Innovation, Systems and Technologies
SP - 615
EP - 621
BT - Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Systems Engineering Mathematics and Physical Sciences
A2 - Iano, Yuzo
A2 - Saotome, Osamu
A2 - Kemper Vásquez, Guillermo Leopoldo
A2 - Cotrim Pezzuto, Claudia
A2 - Arthur, Rangel
A2 - Gomes de Oliveira, Gabriel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Brazilian Technology Symposium, BTSym 2021
Y2 - 8 November 2021 through 10 November 2021
ER -