  Gait recognition is a biometric technology that identifies individuals in a
video sequence by analysing their style of walking or limb movement. However,
this identification is generally sensitive to appearance changes and
conventional feature descriptors such as Gait Energy Image (GEI) lose some of
the dynamic information in the gait sequence. Active Energy Image (AEI) focuses
more on dynamic motion changes than GEI and is more suited to deal with
appearance changes. We propose a new approach, which allows recognizing people
by analysing the dynamic motion variations and identifying people without using
a database of predicted changes. In the proposed method, the active energy
image is calculated by averaging the difference frames of the silhouette
sequence and divided into multiple segments. Affine moment invariants are
computed as gait features for each section. Next, matching weights are
calculated based on the similarity between extracted features and those in the
database. Finally, the subject is identified by the weighted combination of
similarities in all segments. The CASIA-B Gait Database is used as the
principal dataset for the experimental analysis.
