Simple hybrid classifier for face recognition with adaptively generated virtual data
This paper presents a simple hybrid classifier for face recognition with artificially generated virtual training samples. Two sub-classifiers that work on eigenface space, use angular information obtained from training samples and the query feature point. First, training data set was expanded by adding virtual training samples generated adaptively according to the spatial distribution of each person's training samples. Second, a classifier, called the nearest feature angle (NFA) method, finds the most similar sample from an augmented training set to the query sample. Third, after finding the best matched feature line by applying the nearest feature line (NFL) method, the modified nearest feature line (MNFL) method finds the angular information between the query feature point and its projection onto best matched feature line. Finally, the hybrid classifier determines the class by comparing the angular information obtained by the two sub-classifiers. The proposed hybrid classifier exhibits an average error rate of 4.05%, which is 80.2% of that of the standard NFL method with improved robustness for different test sets of facial images.
hybrid classifier face recognition classification virtual sample