A comparative evaluation of Average Face on holistic and local face recognition approaches

This study focuses on a recent paper ldquo100% Accuracy in Automatic Face Recognitionrdquo published on Science, in which an ldquoAverage Facerdquo is proposed and claimed to be capable of dramatically improving performance of a face recognition system. To reveal its working mechanism, we perform the averaging process using pose-varied synthetic images generated from 3D face database and conduct a comparative study to observe its effectiveness on holistic and local face recognition approaches. Two representative methods, i.e. eigenface and local binary pattern (LBP) are employed to perform the experiments. It is interesting to find from our experiments that the performance of the ldquoAverage Facerdquo is not independent of the face recognition approaches. Although face averaging increases the recognition accuracy of eigenface method, it impairs the performance of LBP method.