The use of biometrics has become a popular method to counter the threat of terrorism. Biometric features can be used to confirm identity (biometric authentication) or to identify an individual (biometric identification). Such features include physical (e.g. fingerprints and DNA) and behavioural characteristics (e.g. handwriting and gait). This paper is concerned with one type of behavioural characteristics, namely gait. Gait refers to an individual's style or manner of walk and thus represents one of the body language information signifiers that can identify individuals distinctively in surveillance videos. Recently, gait recognition for individual identification has received increased attention from biometrics researchers as gait can be captured at a distance by using a low-resolution camera. Human gait properties can be affected by clothing and the carrying of objects both of which have been identified within the existing literature as causing difficulties in gait recognition. In this paper, we propose a novel method that generates dynamic and static feature templates of the sequences of silhouette images called Dynamic Static Silhouette Templates (DSSTs) to overcome these difficulties. Here, the DSST is calculated from Gait Energy Images. DSSTs capture the dynamic and the static characteristics of an individual's gait. The experimental results show that our method overcomes the issues arising from differing clothing and the carrying of objects.
|Number of pages||17|
|Journal||Behavioral Sciences of Terrorism and Political Aggression|
|Early online date||22 Sep 2015|
|Publication status||Published - 2015|
- gait recognition