Nonlinear Fringe-adjusted JTC Based Face Tracking Using an Adaptive Decision Criterion

In this paper, we propose a new technique for rotation invariant recognition and tracking of the face of a target person in a given scene. We propose an optimized method for face tracking based on the Fringe-adjusted JTC architecture. To validate our approach, we used the PHPID data base containing faces with various in-plane rotations. To enhance the robustness of the proposed method, we used a three-step optimization technique: (1) utilized the fringe-adjusted filter (H FAF) in the Fourier plane, (2) added nonlinearity in the Fourier plane after applying the HFAF filter, and (3) used a new decision criterion in the correlation plane by considering the correlation peak energy and five largest peaks outside the highest correlation peak. Several tests were made to reduce the number of reference images needed for fast tracking while ensuring robust discrimination and efficient of the desired target.