People Innovation Excellence

COMPRESSIVE SENSING MENGGUNAKAN OPTIMIZED SENSING MATRIX UNTUK VERIFIKASI WAJAH

Member :

  • Jeffry
  • Kelvin Wongso
  • Tommy

Abstract

Biometric appears as one of the solutions which is capable in solving problems that occurred in the usage of password in terms of data access, for example there is possibility in forgetting password and hard to recall various different passwords. With biometrics, physical characteristics of a person can be captured and used in the identification process. In this research, facial biometric is used in the verification process to determine whether the user has the authority to access the data or not. Facial biometric is chosen as its low cost implementation and generate quite accurate result for user identification. Face verification system which is adopted in this research is Compressive Sensing (CS) technique, in which aims to reduce dimension size as well as encrypt data in form of facial test image where the image is represented in sparse signals. Encrypted data can be reconstructed using Sparse Coding algorithm. Two types of Sparse Coding namely Orthogonal Matching Pursuit (OMP) and Iteratively Reweighted Least Squares-ℓp (IRLS-ℓp) will be used for comparison face verification system research. Reconstruction results of sparse signals are then used to find Euclidean norm with the sparse signal of user that has been previously saved in system to determine the validity of the facial test image. Results of system accuracy obtained in this research are 99% in IRLS with time response of face verification for 4,917 seconds and 96,33% in OMP with time response of face verification for 0,4046 seconds with non-optimized sensing matrix, while 99% in IRLS with time response of face verification for 13,4791 seconds and 98,33% for OMP with time response of face verification for 3,1571 seconds with optimized sensing matrix.

 

Key Words

Face Biometric, Compressive Sensing, Orthogonal Matching Pursuit, Iteratively Reweighted Least Square, Face Verification System

 

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