Stature Prediction using Shoe Print Dimensions of an Adult Nigerian Population

Okubike, Emeka Ambrose; Nandi, Michael Ebe; Iheaza, Euphemia C; Obun, Obun C.

doi:10.26735/16586794.2018.024

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Shoeprint images have been studied in many areas, including forensic podiatry [45,86,104], biological traits examination and investigation [62], gender prediction [5,8,63], and body morphological studies [95]. A number of operations underlying shoeprints have been performed including retrieving, recognition, pattern matching via different approaches, and these operations have been performed by many approaches, including manual [3], semi-automated [2,24], automated [1,68], and machine learning (ML) (in particular DL methods) [15,97,104].…”

Section: Literature Studymentioning

confidence: 99%

“…Thus, the learning process of high dimensional space is optimized by the content and perceptual objective function followed by the discriminator structure. The inclusion of (1 − λ • SSIM, see loss-function 3.2) [62] at higher-level tunes the network parameters and bring contextual similarities between given and generated images.…”

Section: Refinement and High-fidelity Image Generationmentioning

confidence: 99%

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GUV-Net for high fidelity shoeprint generation

Hassan

Wang

Pang

et al. 2021

Complex Intell. Syst.

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Shoeprints contain valuable information for tracing evidence in forensic scenes, and they need to be generated into cleaned, sharp, and high-fidelity images. Most of the acquired shoeprints are found with low quality and/or in distorted forms. The high-fidelity shoeprint generation is of great significance in forensic science. A wide range of deep learning models has been suggested for super-resolution, being either generalized approaches or application specific. Considering the crucial challenges in shoeprint based processing and lacking specific algorithms, we proposed a deep learning based GUV-Net model for high-fidelity shoeprint generation. GUV-Net imitates learning features from VAE, U-Net, and GAN network models with special treatment of absent ground truth shoeprints. GUV-Net encodes efficient probabilistic distributions in the latent space and decodes variants of samples together with passed key features. GUV-Net forwards the learned samples to a refinement-unit proceeded to the generation of high-fidelity output. The refinement-unit receives low-level features from the decoding module at distinct levels. Furthermore, the refinement process is made more efficient by inverse-encoded in high dimensional space through a parallel inverse encoding network. The objective functions at different levels enable the model to efficiently optimize the parameters by mapping a low quality image to a high-fidelity one by maintaining salient features which are important to forensics. Finally, the performance of the proposed model is evaluated against state-of-the-art super-resolution network models.

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Section: Literature Studymentioning

confidence: 99%

Section: Refinement and High-fidelity Image Generationmentioning

confidence: 99%

GUV-Net for high fidelity shoeprint generation

Hassan

Wang

Pang

et al. 2021

Complex Intell. Syst.

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“…Recently, deep learning methods have been applied in processing, which include features extraction, reconstruction and investigation of shoeprints; furthermore, these methods have achieved some good results [10], [11], [12], [13] leading to more extensive studies on shoeprints matching [14], recognition [15], and reconstruction [11], [16]. Some of these studies are carried out for crime-scene investigation, such as forensic podiatry [10], [14], [17], [18], [19] while others focus on biological trait examination and investigation, such as stature estimation [20], gender prediction [19], [21], [22] and body morphology examination [23]. Unlike other biometric modalities, such as fingerprints, palm-prints, and retina • M. Hassan, Y. Wang, Y. Liang, and Y. Zhou are with the College of Computer Science and Technology, Jilin University, Changchun, China.Email: mhassandev@gmail.com, wy6868@jlu.edu.cn,ycliang@jlu.edu.cn, zyou@jlu.edu.cn • D. Wang is a member of Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapor.E-mail: WangDi@ntu.edu.sg • D. Li is the CEO of Everspray Science and Technology Company Ltd., Dalian, China.E-mail: lidaixi@everspry.com • D. Xu is a member of AAAS, AIMBE, Department of Electrical Engineering and Computer Science, University of Missouri-Columbia Columbia, USA.E-mail: xudong@missouri.edu prints, shoeprints have inconsistent patterns, shape, and appearance, except for wear-effects.…”

Section: Introductionmentioning

confidence: 99%

“…Aging is significantly associated with gait patterns in the capacity of stances, bipedal floor contact, step length, posture stooping during walking, gender and ethnicity [35], [36], [37], [38], [39]. It is known that gait speed [40], step rate [41], stance and gait-based widening [35] are related to aging [20], [42]. Some studies have reported that in normal walking, elder people (age > 65 years) prefer 41% wider steps than younger subjects (age < 30 years) [27], [34].…”

Section: Introductionmentioning

confidence: 99%

“…Comparative study of the ShoeNet with the standard deep learning modelsThe bold font indicates the best performance in the category. RandomMd randomly took samples in the given age ranges(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). The evaluation metrics Percent-MAE, CS j (f orj = 0, 1, 2, 3..n) and MCS-J (f orJ = 2, 3) are used to examine the significance of under-studied network modalities.…”

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confidence: 99%

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Deep Learning Analysis and Age Prediction from Shoeprints

Hassan

Wang

et al. 2020

Preprint

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Human walking and gaits involve several complex body parts and are influenced by personality, mood, social and cultural traits, and aging. These factors are reflected in shoeprints, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait-pattern disorders, biometrics, and sport studies.

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