Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

Portillo-Portillo, Jose; Leyva, Roberto; Sánchez, Vı́ctor; Sánchez-Pérez, Gabriel; Pérez-Meana, Héctor; Olivares-Mercado, Jesús; Toscano-Medina, Karina; Nakano-Miyatake, Mariko

doi:10.3390/s17010006

Cited by 19 publications

(9 citation statements)

References 35 publications

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“…Therefore, the user naturally performs the activity that is required for recognition even when the system is not in place. There are several ways of capturing this activity: the most commonly studied technologies in gait recognition are video recording [15,16], floor sensors [17,18] and inertial sensors attached to the user [19,20].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Fernandez-Lopez

Liu-Jimenez

Kiyokawa

et al. 2019

Sensors

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In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

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Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Fernandez-Lopez

Liu-Jimenez

Kiyokawa

et al. 2019

Sensors

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“…Viewpoint is considered as the most crucial of those covariate factors [76]. Thus, view-invariance to achieve more reliable gait recognition has been studied by several research groups [19,33,34,43,58,69]. Clothing and carrying conditions are other important covariate factors that are frequently investigated [2,24,53].…”

Section: Background and Relevant Workmentioning

confidence: 99%

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

Kwolek

Michalczuk

Krzeszowski

et al. 2019

Multimed Tools Appl

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We introduce synchronized and calibrated multi-view video and motion capture dataset for motion analysis and gait identification. The 3D gait dataset consists of 166 data sequences with 32 people. In 128 data sequences, each of 32 individuals was dressed in his/her clothes, in 24 data sequences, 6 of 32 performers changed clothes, and in 14 data sequences, 7 of the performers had a backpack on his/her back. In a single recording session, every performer walked from right to left, then from left to right, and afterwards on the diagonal from upperright to bottom-left and from bottom-left to upper-right corner of a rectangular scene. We demonstrate that a baseline algorithm achieves promising results in a challenging scenario, in which gallery/training data were collected in walks perpendicular/facing to the cameras, whereas the probe/testing data were collected in diagonal walks. We compare performances of biometric gait recognition that were achieved on marker-less and marker-based 3D data. We present recognition performances, which were achieved by a convolutional neural network and classic classifiers operating on gait signatures obtained by multilinear principal component analysis. The availability of synchronized multi-view image sequences with 3D locations of body markers creates a number of possibilities for extraction of discriminative gait signatures. The gait data are available at http://bytom.pja.edu.pl/projekty/hm-gpjatk/.

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“…Therefore, the neighboring data points connected in the high-dimensional space are still neighbors in the low-dimensional space after dimensionality reduction. Due to the effectiveness in mapping the data within the high-dimensional space into a low-dimensional manifold, the LEP is better for discovering nonlinear features and manifold structure embedded in the set of data [24] than such linear dimensionality reduction methods as PCA [25], multidimensional scaling [26], and linear discriminant analysis [27].…”

Section: Introductionmentioning

confidence: 99%

Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising

Ding

Zhang

2019

Sensors

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Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

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Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

Cited by 19 publications

References 35 publications

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising

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