A composite spatio-temporal modeling approach for age invariant face recognition

A novel method is proposed that combines both texture and shape features.Face recognition models are built at different levels of data granularity.Experimentation is based on two well-known benchmarks, FG-NET and MORPH.Proposed method outperforms state of art recognition method on rank-1 accuracy.Proposed models support the simulation of aging effects at future time points. In this research we propose a novel method of face recognition based on texture and shape information. Age invariant face recognition enables matching of an image obtained at a given point in time against an image of the same individual obtained at an earlier point in time and thus has important applications, notably in law enforcement. We investigate various types of models built on different levels of data granularity. At the global level a model is built on training data that encompasses the entire set of available individuals, whereas at the local level, data from homogeneous sub-populations is used and finally at the individual level a personalized model is built for each individual. We narrow down the search space by dividing the whole database into subspaces for improving recognition time. We use a two-phased process for age invariant face recognition. In the first phase we identify the correct subspace by using a probabilistic method, and in the second phase we find the probe image within that subspace. Finally, we use a decision tree approach to combine models built from shape and texture features. Our empirical results show that the local and personalized models perform best when rated on both Rank-1 accuracy and recognition time.