Independent components analysis-based nose detection method

Automatic detection of facial features plays an important role in many face-related applications. Among these features, nose region is the least varying part of the human face. In this paper, a method for nose region detection is presented. The method adopt Independent Components Analysis (ICA) as a subspace classifier to classify the face candidate region to nose or non nose. The ICA basis vectors are estimated by the FastICA algorithm. The training has been done using features of nose appearance and shape characterized by the edge information. The effect of preprocessing step on the performance at different dimensions of ICA subspace is also examined. The feasibility of the proposed method has been successfully tested using different databases under various imaging conditions and the results are encouraging.