Gait Recognition for 2-Second Walks Using Viewpoint Normalization and SlidingWindow Process

Many events, such as robberies, missing people, and other suspicious activities, are often captured by cameras. However, these videos are, more often than not, short and not from an optimal angle. Biometric recognition techniques such as facial or iris recognition are inadequate for situations like this. Gait recognition techniques are more suitable than other types of biometrics in such situations. In this work, we propose a new gait recognition technique using viewpoint normalization and a sliding window process. The proposed technique is designed to handle short walking videos captured from any angle. The proposed technique consists of 3 steps. First, a 2-second walk is preprocessed using the sliding window process. This step allows us to generate more gait data in a form of a set of sliding windows from only a 2-second walk. Then sliding windows are transformed into the optimal viewpoint using $\mathit {ViewNet}$ , a proposed neural network designed for finding and transforming sliding windows into the optimal viewpoint angle. Finally, local joint movement information is extracted from sliding windows and used to identify a person using $\mathit {IdenNet}$ , a proposed neural network designed for identifying a person from local joint movements. Four evaluation methods, the top $k$ accuracy test, the precision-recall curves, the cumulative matching characteristic curves, and the gallery-size test, are used to assess the proposed technique. The experimental results show that the proposed technique outperforms existing techniques on all four tests. In particular, the proposed technique can provide a small group, not more than 5, of suspects with a chance above 90% that the real person of interest is in the group. Moreover, the proposed technique still maintains high accuracy even when used with a larger pool of people.