Face verification and identification using DCT-NNDA and SIFT with score-level fusion

This paper proposes a new face verification and identification system based on the fusion of global and local features of face. DCT is used to extract global features from face images. A non-parametric discrimination method, NNDA is applied on the global features to make them more compact within the class clusters; while separating among the class clusters. The effects of expression variations are removed by DWT. It is found that DCT-NNDA is robust to small noisy (blurred) faces, but its performance degrades gradually for variations in scale, rotation, expression and pose. These issues are resolved using local feature extraction through SIFT. The work utilises strengths of DCT-NNDA and SIFT along with score level fusion. The proposed method is robust to scale, small noise, pose, expression, and illumination variations. The proposed system achieves 1% and 0.2% EER along with 99.5% and 98.6% recognition accuracy on ORL and Yale databases, respectively.