The field of forensics aims to understand the physical biomarkers that make each person unique. Recently, it has been discovered that one of the traits that makes us unique from one another are the composition of the microbial communities found throughout our bodies. For example, identical twins who share the same set of DNA may have vastly different microbial communities in or on various body sites. It was recently discovered that microbial communities can be exploited for forensic identification by clustering samples from individual's skin and objects that they may have previously touched. Typically, this is done by using basic multi-dimensional scaling analysis using phylogenetic distances. In this work, we circumvent the use of phylogenetic distances by using the raw community abundances, and we present an application of kernels for metagenomic data analysis. In addition, we show that strategic selection of features can improve classification accuracy.