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Abstract
Abstract Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency.
Highlights
Gait recognition, aiming to identify humans at a distance by inspecting their walking manners, has recently received increasing attentions [17]
(1) We propose a new deep learning based distance metric learning method, called Deep Large Margin Nearest Neighbor, which is the improvement of the famous LMNN. (2) An elaborate learning framework and training algorithm are provided for DLMNN. (3) DLMNN is applied for gait recognition and achieves competitive performance on a set of evaluation experiments
We propose a Deep Large Margin Nearest Neighbor (DLMNN) method to extract robust and discriminant features for gait recognition
Summary
Gait recognition, aiming to identify humans at a distance by inspecting their walking manners, has recently received increasing attentions [17]. Compared with other biometrics (e.g., facial, iris, fingerprint), human gait has some important advantages: 1) it can work well at a distance when other biometrics are obscured or the resolution is insufficient; 2) it is difficult to imitate or camouflage because it is people’s long standing habit; 3) it is non-intrusive as it does not require the cooperation of the subject. To improve the accuracy of successful matching gait features, a distance metric learning method such as large margin nearest neighbor (LMNN) [24] can be applied to reduce the intra-subject variation and increase the inter-subject variation. Benefit from DL, in this paper, we employ deep convolutional neural networks instead of linear transformation of LMNN to learn the metric space, which is termed as Deep Large Margin Nearest Neighbor (DLMNN).
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