Biometric Recognition Using Deep Learning: A Survey <s> Gait Recognition <s> As the remarkable characteristics of remote accessed, robust and security, gait recognition has gained significant attention in the biometrics based human identification task. However, the existed methods mainly employ the handcrafted gait features, which cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed a Siamese neural network based gait recognition framework to automatically extract robust and discriminative gait features for human identification. Different from conventional deep neural network, the Siamese network can employ distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. In particular, to further learn effective model with limited training data, we composite the gait energy images instead of raw sequence of gaits. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-arts. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Gait Recognition <s> 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. <s> BIB002
Gait recognition is a popular pattern recognition problem and attracts a lot of researchers from different communities such as computer vision, machine learning, biomedical, forensic studying and robotics. This problem has also great potential in industries such as visual surveillance, since gait can be observed from a distance without the need for the subject's cooperation. Similar to other behavioral biometrics, it is difficult, however possible, to try to imitate someone else's gait BIB001 . It is also possible for the gait to change due to factors such as the carried load, injuries, clothing, walking speed, viewing angle, and weather conditions,  , BIB002 . It is also a challenge to recognize a person among a group of walking people  . Gait recognition can be model-based, in which the the structure of the subject's body is extracted (meaning more compute demand), or appearance-based, in which features are extracted from the person's movement in the images BIB002 , BIB001 .