Feature Extraction of Face Value Through Gray-Level Co-Occurence Matrix

Abstract

Human Face Recognition is hastily improving in day-to-day life. Digital Image Processing (DIP) is a rapidly evolving field with blooming applications inScience and Engineering. The accuracy of human face recognition system is mostly affected by varying lighting conditions. To overcome the illumination invariant problems and different poses and details, wavelet decomposition method was used. At various scales and frequencies the facial features are extracted by multi-resolution property of Discrete Wavelet Transform (DWTs). The wavelet sub bands are used to represent the well-lit face images. Fusion of match scores depends on low and high frequency which is based on the human face representation to improve the accuracy in varying lighting conditions. For obtaining better performance and accuracy in human face recognition under different illumination conditions, here this project contributes by using adaptive face recognition. Wavelet decomposition was performed to attain the image accuracy and efficiency.  GLCM algorithm was enriched for calculating texture features of an image. For effective classification of different human faces, K-Nearest Neighbour classifier was used. The recognition rate of K-NN is 91%.