Single 2D pressure footprint based person identification

Wang, Xin‐Nian; Wang, Huiyu; Cheng, Qi; Nankabirwa, Namusisi Linda; Zhang, Tao

doi:10.1109/btas.2017.8272725

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…The classification model using raw data does not guarantee good accuracy; thus, there is the need for a pre-processing process that removes unnecessary parts which need not to be learned, and it is necessary to extract the ROI. In particular, because there are limitations with respect to the collection of sufficient data from experimental and clinical data, the preprocessing process takes a long time before using algorithms to achieve high accuracy [28][29][30]. Because the location and shape of the collected data varied owing to differences in the size, shape, and posture of each subject's feet, the location and foot angle must be corrected to improve learning accuracy [29][30][31].…”

Section: Pre-processing Of Image Data (Distribution Of Pressure)mentioning

confidence: 99%

“…Then, the inner line of the foot (red line) is defined by connecting the outermost points of each foot. Next, the foot image is rotated based on the center of each image using the rotation matrix in Equation (2) [ 29 ]. At this time,

is applied when rotating in a counterclockwise direction, and

is applied when rotating in a clockwise direction.…”

Section: Data Preparationmentioning

confidence: 99%

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A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data

Chae

Kang

Noh

2020

Sensors

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The human foot is easily deformed owing to the innate form of the foot or an incorrect walking posture. Foot deformations not only pose a threat to foot health but also cause fatigue and pain when walking; therefore, accurate diagnoses of foot deformations are required. However, the measurement of foot deformities requires specialized personnel, and the objectivity of the diagnosis may be insufficient for professional medical personnel to assess foot deformations. Thus, it is necessary to develop an objective foot deformation classification model. In this study, a model for classifying foot types is developed using image and numerical foot pressure data. Such heterogeneous data are used to generate a fine-tuned visual geometry group-16 (VGG16) and K−nearest neighbor (k-NN) models, respectively, and a stacking ensemble model is finally generated to improve accuracy and robustness by combining the two models. Through k-fold cross-validation, the accuracy and robustness of the proposed method have been verified by the mean and standard deviation of the f1 scores (0.9255 and 0.0042), which has superior performance compared to single models generated using only numerical or image data. Thus, the proposed model provides the objectivity of diagnosis for foot deformation, and can be used for analysis and design of foot healthcare products.

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Section: Pre-processing Of Image Data (Distribution Of Pressure)mentioning

confidence: 99%

is applied when rotating in a counterclockwise direction, and

is applied when rotating in a clockwise direction.…”

Section: Data Preparationmentioning

confidence: 99%

A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data

Chae

Kang

Noh

2020

Sensors

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“…In addition, it is recommended that footwear can be used jointly with other biometrics features to enhance the overall performance. Another person identification systems proposed based on footprint using a single bare or socked footprint is proposed [8]. The researchers used both of Pressure Radial Gradient Map and Geometrical Shape Spectrum Representation to simulate a footprint.…”

Section: Related Workmentioning

confidence: 99%

“…The human footprint carries many characteristics that play important roles in forensic investigation. For an example of these characteristics the walking habits or standing, skin texture of the foot sole and anatomical structures of the foot [8]. Therefore, these characteristics help to overcome an increasing happening issue in hospitals, birthing centers, health centers where multiple births occur simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…However, the footprint shows some advantages of animal recognition such as stated in [11] and [12]. One of the main challenges in recognizing the animals through the footprint is the features extraction from the images, which are usually located in the boundary curve of the footprints [8]. In general, there are several features can be used to identify the animal through the footprints such as the number and the size of blobs which are usually used by the humans to identify the animals in reality.…”

Section: Introductionmentioning

confidence: 99%

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Deep transfer learning for human identification based on footprint: A comparative study

Abuqadumah¹,

Ali²,

Almisreb³

et al. 2019

PEN

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Identifying people based on their footprint has not yet gained enough attention from the researchers. Therefore, in this paper, an investigation of human identification conducted based on the footprint. Transfer Learning used as the main concept of this investigation. The aim of using Transfer Learning is to overcome the need for a large-scale dataset and achieve high accuracy with a low-scale dataset. Five well-known models used, namely, Alexnet, Vgg16, Vgg19, Googlenet, and Inception v3. Each of these models fine-tuned to fit-in the paper's topic. A dataset of 30 individuals constructed in order to train the models. The right and left footprint of each individual captured with iPhone camera. The models trained and evaluated based on the same settings. The evaluation shows that Inception v3 model achieved the highest accuracy compared to all other four models.

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