A Feature Vector Compression Approach for Face Recognition using Convolution and DWT

Authors

  • Ganapathi Sagar Dr. AIT Bangalore
  • Savita Y Barker Sai Tektronix Pvt Ltd,Bangaluru
  • K B Raja University Visvesvaraya College of Engineering,Bangaluru University,Bangaluru
  • K Suresh Babu University Visvesvaraya College of Engineering,Bangaluru University,Bangaluru
  • Venagopal K R University Visvesvaraya College of Engineering,Bangaluru University,Bangaluru

DOI:

https://doi.org/10.24297/ijct.v15i1.1709

Keywords:

Biometrics, face recognition, DWT, convolution, vector compression

Abstract

The biometric identification of a person using face trait is more efficient compared to other traits as the co-operation of a person is not required. In this paper, we propose a feature vector compression approach for face recognition using convolution and DWT.The one level DWT is applied on face images and considered only LL band. The normalized technique is applied on LL sub band to reduce high value coefficients into lower range of values ranging between Zero and one. The novel concept of linear convolution is applied on original image and LL band matrix to enhance quality of face images to obtain unique features. The Gaussian filter is applied on the output of convolution block to reduce high frequency components to generate fine-tuned feature vectors. The numbers of feature vectors of many samples of single person are converted into a single vector which reduces number of features of each person. The Euclidean distance is used to compare test image features with features of database persons to compute performance parameters. It is observed that the performance recognition rate is high compared to existing techniques.

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Published

2015-11-01

How to Cite

Sagar, G., Y Barker, S., Raja, K. B., Babu, K. S., & K R, V. (2015). A Feature Vector Compression Approach for Face Recognition using Convolution and DWT. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 15(1), 6453-6470. https://doi.org/10.24297/ijct.v15i1.1709

Issue

Section

Research Articles