Human face gender identification using Thepade's sorted N-ary block truncation coding and machine learning classifiers

Human face gender identification is increasingly gaining popularity because of exponential interest in ubiquitous and pervasive computing. The computing embedded in environment can feel the person's presence and as per person being male or female, may induce certain decisions with help of ubiquitous computing devices to make environment suited to person. The challenge of detecting a face is male or female is very trivial due to similarity of features. This work presents use of Thepade's Sorted Block Truncation Coding N-ary (TSBTC N-ary) for face feature extraction and further deploys machine learning classifiers to identify face as male or female. In proposed face gender identification, TSBTC N-ary is explored with six combinations (from two-ary to seven-ary) for face feature extraction with fourteen machine learning classifiers giving 96 variations; tested using Faces94-dataset. Classification accuracy is used as performance measure. Overall Random Forest gives best performance and TSBTC-7ary outperforms other feature extraction variations.