Human gait recognition based on gait flow image considering walking direction

To improve the human gait recognition rate, a method based on the modified gait flow image (GFI) considering the walking direction is used in this paper. The walking direction depends on the height and position at the walking beginning point and ending point in a gait cycle. GFI is generated to represent the motion characteristics of a gait without constructing any model. Lacus-Kanade´s approach is used to calculate the optical flow field of GFI. In recognition phase, a database about GFIs at different walking directions of the target is established in advance. After computing the new person´s walking direction and GFI, we look up the database to find GFI of the target according to the new person´s walking direction. Then Linear Discriminant Analysis (LDA) is used to reduce the dimension. At last we can recognize whether the new person is the target through computing the similarity between the new GFI and the target´s GFI. The performance of this method was evaluated on the data in the CASIA B database and the data obtained in lab environment. The experimental results prove that the estimated walking direction is quite correct, and the GFI computed by Lacus-Kanade´s approach is an efficient gait representation for gait recognition.