TY - GEN
T1 - Face mask recognition with realistic fabric face mask data set
T2 - 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021
AU - Lionnie, Regina
AU - Apriono, Catur
AU - Gunawan, Dadang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/21
Y1 - 2021/4/21
N2 - Wearing a mask is a requirement in the Covid-19 pandemic for the general public. While it is one of the several must-do actions to prevent forward spread in the Covid-19 infections, at the same time, the effect of wearing a mask in naïve face recognition systems have shown lower system performance in several cases and conditions. Simultaneously, only a handful of research studies have focused on a non-medical face mask with realistic images data set. This research proposed a new data set of realistic fabric face mask data set to be evaluated using surface curvature and gray level co-occurrence matrix (GLCM). The classification applied support vector machine (SVM). One hundred seventy-six images in the data set were analyzed with various properties, resulting in several experiments. The experiments' parameters were color properties, approaches in surface curvature, i.e., Gaussian, mean and principal curvature, angle and distance in GLCM, GLCM properties, i.e., contrast, homogeneity, correlation and energy, also kernel functions in SVM. The best accuracy result, 87.5%, was derived from the combinations of these parameters. This research also improved the running time of the recognition process while maintaining the system's performance.
AB - Wearing a mask is a requirement in the Covid-19 pandemic for the general public. While it is one of the several must-do actions to prevent forward spread in the Covid-19 infections, at the same time, the effect of wearing a mask in naïve face recognition systems have shown lower system performance in several cases and conditions. Simultaneously, only a handful of research studies have focused on a non-medical face mask with realistic images data set. This research proposed a new data set of realistic fabric face mask data set to be evaluated using surface curvature and gray level co-occurrence matrix (GLCM). The classification applied support vector machine (SVM). One hundred seventy-six images in the data set were analyzed with various properties, resulting in several experiments. The experiments' parameters were color properties, approaches in surface curvature, i.e., Gaussian, mean and principal curvature, angle and distance in GLCM, GLCM properties, i.e., contrast, homogeneity, correlation and energy, also kernel functions in SVM. The best accuracy result, 87.5%, was derived from the combinations of these parameters. This research also improved the running time of the recognition process while maintaining the system's performance.
KW - Covid-19
KW - Face mask recognition
KW - Gray level co-occurrence matrix
KW - Support vector machine
KW - Surface curvature
UR - http://www.scopus.com/inward/record.url?scp=85106678070&partnerID=8YFLogxK
U2 - 10.1109/IEMTRONICS52119.2021.9422532
DO - 10.1109/IEMTRONICS52119.2021.9422532
M3 - Conference contribution
AN - SCOPUS:85106678070
T3 - 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings
BT - 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings
A2 - Chakrabarti, Satyajit
A2 - Paul, Rajashree
A2 - Gill, Bob
A2 - Gangopadhyay, Malay
A2 - Poddar, Sanghamitra
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 April 2021 through 24 April 2021
ER -