Analysis of Wavelets, Brushlets and Beamlets for Feature Extraction in Face Recognition
Pavan Chavan, Namratha M R · 2015
Face Recognition is a very challenging area and has a wide range of applications in various fields such as video surveillance systems, crime informatics, etc. Feature extraction is the basic step in any face recognition system. It is required because the number of inputs or size of input may be too large and hence we extract only features of interest. Texture based feature extraction is preferred due to it’s invariance to pose, light, reflection. Beamlets and Brushlets are used for feature extraction in Face Recognition. Beamlets follow a hierarchical approach. Brushlets use Windowed Fourier Transform to obtain local optimization. This local approach provides better accuracy than global methods. They are generally used in the field of image compression. Both provide better recognition rate than the commonly used wavelets.