Enhancing face recognition from video sequences using robust statistics
Sid-Ahmed Berrani, Christophe García · 2006
The aim of this work is to investigate a way of enhancing the performance of face recognition from video sequences by selecting only well-framed face images from those extracted from video sequences. It is known that noisy face images (e.g. not well-centered, non-frontal poses...) significantly reduce the performance of face recognition methods, and therefore, need to be filtered out during the training and the recognition. The proposed method is based on robust statistics, and more precisely, a recently proposed robust high-dimensional data analysis method, RobPCA. Experiments show that this filtering procedure improves the recognition rate by 10 to 20%.