Skin recognition is used in many applications ranging from algorithms for face detection, predicting age, gender classification, and to objectionable image filtering. These data collections are growing rapidly and can therefore be considered as spatial data streams. For data stream classification, time is a major issue. However, these spatial data sets are too large to be classified effectively in a reasonable amount of time using existing methods. In this work, a novel and computational fast algorithm is proposed for predicting age of humans with PeanoCount Tree (P-Tree). The predicting system was developed and tested based on texture features extracted Local Gradient Patterns (LGP) and Gray Level Cooccurrence Matrix (GLCM) to give better and more predicting accuracy with a range of time period. The P-Tree is a spatial data organization that provides a lossless compressed representation of a spatial data set and facilitates efficient classification and other data mining techniques. Using P-tree structure, fast calculation of measurements, such as information gain, can be achieved. The authors compare P-tree decision tree induction classification and a classical decision tree induction method with respect to the speed at which the classifier can be built (and rebuilt when substantial amounts of new data arrive). Experimental results show that the P-tree method is significantly faster than existing classification methods, making the preferred method for mining on spatial data streams.