Proceedings ArticleDOI
On combining gait features
Yasushi Makihara,Daigo Muramatsu,Haruyuki Iwama,Yasushi Yagi +3 more
- pp 1-8
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TLDR
The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks.Abstract:
This paper describes a method of gait recognition using multiple gait features in conjunction with score-level fusion techniques. More specifically, we focus on the state-of-the-art period-based gait features such as a gait energy image, a frequency-domain feature, a gait entropy image, a chrono-gait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches including not only conventional transformation-based approaches (e.g., sum-rule and min-rule) but also classification-based approaches (e.g., support vector machine) and density-based approaches (e.g., Gaussian mixture model, kernel density estimation, linear logistic regression). In experiments, the large-population gait database with 3,249 subjects was used to measure the performance improvement in a statistically reliable way. The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks.read more
Citations
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Ching-Han Chen,Chia Te Chu +1 more
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Cloth invariant gait recognition using pooled segmented statistical features
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Proceedings ArticleDOI
Literature Review: Model Free Human Gait Recognition
TL;DR: This paper provides a recent comprehensive survey of only model free gait recognition approach, which focuses on motionfree gait image representation, dimensionality reduction of extracted feature and classification.
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A New Gait-Based Identification Method Using Local Gauss Maps
TL;DR: This work proposes a new descriptor for human identification based on gait, and thoroughly demonstrates the superiority of the method in gait-based human identification compared to state-of-the-art approaches.
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Plant identification using score-based fusion of multi-organ images
TL;DR: A fusion technique for species identification from images of different plant organs given a series of image organs using a state-of-the-art deep convolutional neural network and a hybrid fusion model is described.
References
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