DIFFERENT TEXTURE CLASSIFICATION AND AGE PREDICTION OF FACE IMAGES USING PEANOCOUNT DECISION CLASSIFIER
BasavaRaju K
Research Scholar, JNTUK, A.P. India
Dr. Y. RamaDevi
HOD in CSE Dept., CBIT, Gandipet, Hyderabad.
Dr. P.V. Kumar
Professor in CSE Dept, OU, Hyderabad
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Recognition of skin is used in many applications ranging from algorithms for predicting age , face detection, gender classification, and to objectionable image filtering. These data collections are growing rapidly and can therefore be considered as spatial data streams. Time is a major issue for data stream classification,. 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 PeanoCountTree (P-Tree). The predicting system was developed and tested based on texture features extracted local gradient patterns (LGP) and gray level co-occurrence matrix (GLMC) to give better and more predicting accuracy with a range of time period. The PTree 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. We 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 it the preferred method for mining on spatial data streams.
Keywords: Feature extraction; Gradient Operator; GLCM; P-Classifier, bSQ, Peano Count Tree, Mining
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