1. A method of identifying an individual using gait analysis comprising: receiving, via an invariant processor, acceleration data from an inertial sensor proximate to an individual, wherein the acceleration data corresponds to a gait of the individual; calculating, via the invariant processor, rotational invariant values based, at least in part, on the acceleration data, wherein the rotational invariant values are independent of a orientation of the inertial sensor proximate to the individual; calculating, via the invariant processor, inner product values according to: 
 I inner ({right arrow over (A(t 1 ))}, {right arrow over (A(t 2 ))})=<{right arrow over (A(t 1 ))}, {right arrow over (A(t 2 ))}> where {right arrow over (A(t 1 ))}, =[x(t 1 ) y(t 1 ) z(t 1 )] t and {right arrow over (A(t 2 ))}=[x(t 2 ) y(t 2 ) z(t 2 )] t are two 3d acceleration vectors at times t 1 and t 2 ; generating, via a gait dynamic image processor, a gait dynamics image (GDI) based, at least in part, on the rotational invariant values; identifying, via the gait dynamic image processor, a quasi-periodic nature of the gait signals under a general energy minimization paradigm with a Bayesian interpretation; building, via the gait dynamic image processor, pace independent GDIs to achieve invariance to sensor orientation and gait speed; matching, via a matching processor, the GDI to one of a set predetermined gait data to identify the individual; and outputting, via an output line, a result from the matching processor.