Empirical remarks on output coding methods for face recognition

Since facial images are affected by various factors, the representation capacity for face database is limited by the prototypes collected for training. Therefore, to extend the capacity covering variations of facial images, we should adopt a complex classifier. It is desirable to use output coding method by considering the number of classes changes. We propose new output coding methods and then compare them with representative conventional output coding methods to investigate the properties of decomposition schemes through the experiment on the ORL face dataset. Finally, we give discussions on some factors that should be considered in the designing of decomposition scheme, to provide some foundation for designing new output coding methods in face recognition.