535 research outputs found
Gait Recognition from Motion Capture Data
Gait recognition from motion capture data, as a pattern classification
discipline, can be improved by the use of machine learning. This paper
contributes to the state-of-the-art with a statistical approach for extracting
robust gait features directly from raw data by a modification of Linear
Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU
MoCap database show that the suggested method outperforms thirteen relevant
methods based on geometric features and a method to learn the features by a
combination of Principal Component Analysis and Linear Discriminant Analysis.
The methods are evaluated in terms of the distribution of biometric templates
in respective feature spaces expressed in a number of class separability
coefficients and classification metrics. Results also indicate a high
portability of learned features, that means, we can learn what aspects of walk
people generally differ in and extract those as general gait features.
Recognizing people without needing group-specific features is convenient as
particular people might not always provide annotated learning data. As a
contribution to reproducible research, our evaluation framework and database
have been made publicly available. This research makes motion capture
technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia
Computing, Communications, and Applications (TOMM), special issue on
Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin
note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392,
arXiv:1609.0693
Gait recognition using kinect and locally linear embedding
This paper presents the use of locally linear embedding (LLE) as feature extraction technique for classifying a person’s identity based on their walking gait patterns. Skeleton data acquired from Microsoft Kinect camera were used as an input for (1). Multilayer Perceptron (MLP) and (2). LLE with MLP. The MLP classification accuracy result was used for comparison between both. Several MLP and LLE properties were tested to find the optimal number of setting that can improve the MLP performance. Based on the two methods used, the neural network implemented with LLE showed the better accuracy compared to the neural network alone.Keywords: locally linear embedding; neural network; multilayer perceptron
GAIT Technology for Human Recognition using CNN
Gait is a distinctive biometric characteristic that can be detected from a distance; as a result, it has several uses in social security, forensic identification, and crime prevention. Existing gait identification techniques use a gait template, which makes it difficult to keep temporal information, or a gait sequence, which maintains pointless sequential limitations and loses the ability to portray a gait. Our technique, which is based on this deep set viewpoint, is immune to frame permutations and can seamlessly combine frames from many videos that were taken in various contexts, such as diversified watching, angles, various outfits, or various situations for transporting something. According to experiments, our single-model strategy obtains an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset when used under typical walking conditions. Our model also demonstrates a great degree of robustness under numerous challenging circumstances. When carrying bags and wearing a coat while walking, it obtains accuracy on the CASIA-B of 90.8% and 70.3%, respectively, greatly surpassing the best approach currently in use. Additionally, the suggested method achieves a satisfactory level of accuracy even when there are few frames available in the test samples; for instance, it achieves 85.0% on the CASIA-B even with only 7 frames
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