A system for identification of an individual, the system comprising: a processor; and a gait-pose modeler coupled to the processor, to obtain one or more static feature vectors and one or more dynamic feature vectors for skeleton data frames of the individual performing a step activity, wherein the skeleton data frames comprise skeleton data points containing information about the skeleton joints of the individual; create, for the individual, a first predefined number of clusters based on clustering of the dynamic feature vectors for the skeleton data frames, wherein the first predefined number is a number of poses to be identified in the step activity; create, for the individual, a second predefined number of sub-clusters within each of the clusters based on clustering of the dynamic feature vectors for the skeleton data frames associated with each of the clusters, wherein the second predefined number is a number of sub-poses to be identified in the poses; and determine, for the individual, a gait-pose feature data set based on computation of i) a center of the dynamic feature vectors for the skeleton data frames associated with each of the sub-clusters, and ii) a mean of the static feature vectors for the skeleton data frames associated with each of the clusters for identifying the individual uniquely. a processor; and a gait-pose modeler coupled to the processor, to obtain one or more static feature vectors and one or more dynamic feature vectors for skeleton data frames of the individual performing a step activity, wherein the skeleton data frames comprise skeleton data points containing information about the skeleton joints of the individual; create, for the individual, a first predefined number of clusters based on clustering of the dynamic feature vectors for the skeleton data frames, wherein the first predefined number is a number of poses to be identified in the step activity; create, for the individual, a second predefined number of sub-clusters within each of the clusters based on clustering of the dynamic feature vectors for the skeleton data frames associated with each of the clusters, wherein the second predefined number is a number of sub-poses to be identified in the poses; and determine, for the individual, a gait-pose feature data set based on computation of i) a center of the dynamic feature vectors for the skeleton data frames associated with each of the sub-clusters, and ii) a mean of the static feature vectors for the skeleton data frames associated with each of the clusters for identifying the individual uniquely. obtain one or more static feature vectors and one or more dynamic feature vectors for skeleton data frames of the individual performing a step activity, wherein the skeleton data frames comprise skeleton data points containing information about the skeleton joints of the individual; create, for the individual, a first predefined number of clusters based on clustering of the dynamic feature vectors for the skeleton data frames, wherein the first predefined number is a number of poses to be identified in the step activity; create, for the individual, a second predefined number of sub-clusters within each of the clusters based on clustering of the dynamic feature vectors for the skeleton data frames associated with each of the clusters, wherein the second predefined number is a number of sub-poses to be identified in the poses; and determine, for the individual, a gait-pose feature data set based on computation of i) a center of the dynamic feature vectors for the skeleton data frames associated with each of the sub-clusters, and ii) a mean of the static feature vectors for the skeleton data frames associated with each of the clusters for identifying the individual uniquely.