In this work we present a deep convolutional neural network using 3D convolutions for Gait Recognition in multiple views capturing spatio-temporal features. A special input format, consisting of the gray-scale image and optical flow enhance color invaranice. The approach is evaluated on three different datasets, including variances in clothing, walking speeds and the view angle. In contrast to most state-of-the-art Gait Recognition systems the used neural network is able to generalize gait features across multiple large view angle changes. The results show a comparable to better performance in comparison with previous approaches, especially for large view differences.
Wolf et al. REF employed 3D convolutional neural network to extract the spatio-temporal features used for multi-view gait recognition.
Multi-view gait recognition using 3D convolutional neural networks
2016 IEEE International Conference on Image Processing (ICIP)
2016 IEEE International Conference on Image Processing (ICIP)
Computer Science