Recognition of Occluded Faces Based on Multi-subspace Classification

In the paper we investigate a problem of face recognition in uncontrolled environment – distorted by occlusion, shadows and other local modifications. Such problems are very common for real-world conditions, thus the presented algorithm allows to eliminate them. It is based on dimensionality reduction approach (two-dimensional Karhunen-Loéve Transform) and distance-based classification. We use simple transformations involving face normalization and individual facial regions extraction as a pre-processing. Then, we perform independent recognition of extracted facial regions and combine the results in order to make a final classification. The results of experiments conducted on images taken from 9 publicly available datasets show that a quite simple algorithm is capable of successful recognition without high computing power requirements, as opposite to more sophisticated methods presented in the literature. As it was proved, the presented approach gives significantly better efficiency than a whole image-based recognition.