Wavelet Transformation and Local Binary Pattern for Data Augmentation in Deep Learning-based Face Recognition

Tanzilal Mustaqim, Hilya Tsaniya, Fariz Ardin Adhiyaksa, Nanik Suciati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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%.

Original languageEnglish
Title of host publication2022 10th International Conference on Information and Communication Technology, ICoICT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages362-367
Number of pages6
ISBN (Electronic)9781665481656
DOIs
Publication statusPublished - 2022
Event10th International Conference on Information and Communication Technology, ICoICT 2022 - Virtual, Online, Indonesia
Duration: 2 Aug 20223 Aug 2022

Publication series

Name2022 10th International Conference on Information and Communication Technology, ICoICT 2022

Conference

Conference10th International Conference on Information and Communication Technology, ICoICT 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period2/08/223/08/22

Keywords

  • augmentation
  • deep learning
  • face recognition
  • local binary pattern
  • wavelet transformation

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