Big Data Analysis and Data Mining

Biography

Biography: Mallikarjuan Rao. G

Abstract

Facial features if properly analyzed /mined are capable of providing information about the gender classification, expression classification and age approximation. Each one of the task is challenging for the researchers due to the involved computational effort and dynamism associated with environmental conditions of capturing domain. The recent trends in machine based human facial image processing are concerned with the topics about predicting age, classifying gender, race, and expressions from facial images, and so on. However, machine based approaches have not yet captured intelligence of mining process involved in human brain. More over researchers attempted each one as a separate problem and suggested data dependent solutions. Further the algorithms have not exploring available parallel computational power existing machines multi-core machines. Hence, in this paper we would like to propose unified scalable approach based on artificial neural network with a new pre-processing feature extraction technique, local active pixel pattern, LAPP. Intelligence extracted from pixel level mining is induced in the machine so that after training it is capable of classifying the mood of the person. Heterogeneity of excremental data sets have truly demonstrated the significance of our approach. FERET facial database has been used for gender classification , YALE, JAFFE, Face expression and CMU AMP Face Expression Databases have been used for expression classification and FGNET AGE data set has been used for age approximation.Results are encouraging with more the 90% accuracy.