GEINet: View-Invariant Gait Recognition Using a Convolutional Neural Network

This paper proposes a method of gait cognition using a convolutional neural network (CNN). Inspired by the great successes ofriffs in image recognition tasks, we feed in the most prevalent image-based gait representation, that is, the gait energy image (GET), as an input to a Cnr designed for gait recognition called GEINet. More specifically, GEINet is composed of two sequential triplets gl convolution, pooling, and normalization layers, and two subsequent fully connected layers, which output a set of similarities to individual training subjects. We conducted experiments to demonstrate the effectiveness of the proposed method in terms of cross-view gait recognition in both cooperative and uncooperative settings using the OU-ISIR large population dataset. As a result, we confirmed that the proposed method significantly outperformed state-of-the-art approaches, in particular in verification scenarios.