TY - GEN
T1 - Wavelet Transformation and Local Binary Pattern for Data Augmentation in Deep Learning-based Face Recognition
AU - Mustaqim, Tanzilal
AU - Tsaniya, Hilya
AU - Adhiyaksa, Fariz Ardin
AU - Suciati, Nanik
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Facial recognition is used in many fields such as verification, communication, and other fields. The success of facial analysis is influenced by noise from environmental factors such as illumination, expression, posture, occlusion, and others. Therefore, a solid and effective analytical model is needed to overcome noise using feature extraction and deep learning. This study compares the effect of various wavelet transform methods and local binary pattern (LBP) on facial recognition as additional data on the CNN architecture and the pre-trained VGG16 model on the Yale-B facial recognition dataset. The results showed that the LBP and IDWT features of discrete wavelet transform (DWT) as augmented data resulted in the highest accuracy value of 99.69%.
AB - Facial recognition is used in many fields such as verification, communication, and other fields. The success of facial analysis is influenced by noise from environmental factors such as illumination, expression, posture, occlusion, and others. Therefore, a solid and effective analytical model is needed to overcome noise using feature extraction and deep learning. This study compares the effect of various wavelet transform methods and local binary pattern (LBP) on facial recognition as additional data on the CNN architecture and the pre-trained VGG16 model on the Yale-B facial recognition dataset. The results showed that the LBP and IDWT features of discrete wavelet transform (DWT) as augmented data resulted in the highest accuracy value of 99.69%.
KW - augmentation
KW - deep learning
KW - face recognition
KW - local binary pattern
KW - wavelet transformation
UR - http://www.scopus.com/inward/record.url?scp=85141569650&partnerID=8YFLogxK
U2 - 10.1109/ICoICT55009.2022.9914875
DO - 10.1109/ICoICT55009.2022.9914875
M3 - Conference contribution
AN - SCOPUS:85141569650
T3 - 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
SP - 362
EP - 367
BT - 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information and Communication Technology, ICoICT 2022
Y2 - 2 August 2022 through 3 August 2022
ER -