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Keywords = shoeprint

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17 pages, 2072 KiB  
Article
Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet
by Yujie Shen, Xuemei Jiang, Yabin Zhao and Wenxin Xie
Sensors 2025, 25(15), 4578; https://doi.org/10.3390/s25154578 - 24 Jul 2025
Viewed by 298
Abstract
This study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images characterized by concentrated feature distributions and rich [...] Read more.
This study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images characterized by concentrated feature distributions and rich texture patterns. To address this, our framework integrates an improved StarNet into the backbone of YOLOv8 architecture. Leveraging the unique advantages of element-wise multiplication, the redesigned backbone efficiently maps inputs to a high-dimensional nonlinear feature space without increasing channel dimensions, achieving enhanced representational capacity with low computational latency. Subsequently, an Encoder layer facilitates feature interaction within the backbone through multi-scale feature fusion and attention mechanisms, effectively extracting rich semantic information while maintaining computational efficiency. In the feature fusion part, a feature modulation block processes multi-scale features by synergistically combining global and local information, thereby reducing redundant computations and decreasing both parameter count and computational complexity to achieve model lightweighting. Experimental evaluations on a proprietary barefoot footprint dataset demonstrate that the proposed model exhibits significant advantages in terms of parameter efficiency, recognition accuracy, and computational complexity. The number of parameters has been reduced by 0.73 million, further improving the model’s speed. Gflops has been reduced by 1.5, lowering the performance requirements for computational hardware during model deployment. Recognition accuracy has reached 99.5%, with further improvements in model precision. Future research will explore how to capture shoeprint images with complex backgrounds from shoes worn at crime scenes, aiming to further enhance the model’s recognition capabilities in more forensic scenarios. Full article
(This article belongs to the Special Issue Transformer Applications in Target Tracking)
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20 pages, 6281 KiB  
Article
Overlapping Shoeprint Detection by Edge Detection and Deep Learning
by Chengran Li, Ajit Narayanan and Akbar Ghobakhlou
J. Imaging 2024, 10(8), 186; https://doi.org/10.3390/jimaging10080186 - 31 Jul 2024
Cited by 1 | Viewed by 3061
Abstract
In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded [...] Read more.
In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional convolutional neural networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces and employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds. Full article
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15 pages, 18902 KiB  
Review
Crime Scene Shoeprint Image Retrieval: A Review
by Yanjun Wu, Xianling Dong, Guochao Shi, Xiaolei Zhang and Congzhe Chen
Electronics 2022, 11(16), 2487; https://doi.org/10.3390/electronics11162487 - 10 Aug 2022
Cited by 9 | Viewed by 7391
Abstract
Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to [...] Read more.
Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to the query image. In this way, a shoeprint can not only be used as an exhibit for bringing criminal charges but also to provide a clue to a case. The goal of this work is to present an overview of the existing works conducted in shoeprint image retrieval. We detail the different phases of the shoeprint retrieval task and present a summary of the state-of-the-art methods. We analyzed the difficulties and problems in this field and discussed future work directions. This review may help neophytes become involved in research easily and quickly. Full article
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20 pages, 7015 KiB  
Article
Automatic Retrieval of Shoeprints Using Modified Multi-Block Local Binary Pattern
by Sayyad Alizadeh, Hossein B. Jond, Vasif V. Nabiyev and Cemal Kose
Symmetry 2021, 13(2), 296; https://doi.org/10.3390/sym13020296 - 9 Feb 2021
Cited by 11 | Viewed by 2993
Abstract
A shoeprint is a valuable clue found at a crime scene and plays a significant role in forensic investigations. In this paper, in order to maintain the local features of a shoeprint image and place a pattern in a block, a novel automatic [...] Read more.
A shoeprint is a valuable clue found at a crime scene and plays a significant role in forensic investigations. In this paper, in order to maintain the local features of a shoeprint image and place a pattern in a block, a novel automatic method was proposed, referred to as Modified Multi-Block Local Binary Pattern (MMB-LBP). In this method, shoeprint images are divided into blocks according to two different models. The histograms of all blocks of the first and second models are separately measured and stored in the first and second feature matrices, respectively. The performance evaluations of the proposed method were carried out by comparing with state-of-the-art methods. The evaluation criteria are the successful retrieval rates obtained using the best match score at rank one and cumulative match score for the first five matches. The comparison results indicated that the proposed method performs better than other methods, in terms of retrieval of complete and incomplete shoeprints. That is, the proposed method was able to retrieve 97.63% of complete shoeprints, 96.5% of incomplete toe shoeprints, and 91.18% of incomplete heel shoeprints. Moreover, the experiments showed that the proposed method is significantly resistant to the rotation, salt and pepper noise, and Gaussian white noise distortions in comparison with the other methods. Full article
(This article belongs to the Section Computer)
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20 pages, 5817 KiB  
Article
Multi-Layer Feature Based Shoeprint Verification Algorithm for Camera Sensor Images
by Xinnian Wang, Yanjun Wu and Tao Zhang
Sensors 2019, 19(11), 2491; https://doi.org/10.3390/s19112491 - 31 May 2019
Cited by 10 | Viewed by 4361
Abstract
As a kind of forensic evidence, shoeprints have been treated as important as fingerprint and DNA evidence in forensic investigations. Shoeprint verification is used to determine whether two shoeprints could, or could not, have been made by the same shoe. Successful shoeprint verification [...] Read more.
As a kind of forensic evidence, shoeprints have been treated as important as fingerprint and DNA evidence in forensic investigations. Shoeprint verification is used to determine whether two shoeprints could, or could not, have been made by the same shoe. Successful shoeprint verification has tremendous evidentiary value, and the result can link a suspect to a crime, or even link crime scenes to each other. In forensic practice, shoeprint verification is manually performed by forensic experts; however, it is too dependent on experts’ experience. This is a meaningful and challenging problem, and there are few attempts to tackle it in the literatures. In this paper, we propose a multi-layer feature-based method to conduct shoeprint verification automatically. Firstly, we extracted multi-layer features; and then conducted multi-layer feature matching and calculated the total similarity score. Finally, we drew a verification conclusion according to the total similarity score. We conducted extensive experiments to evaluate the effectiveness of the proposed method on two shoeprint datasets. Experimental results showed that the proposed method achieved good performance with an equal error rate (EER) of 3.2% on the MUES-SV1KR2R dataset and an EER of 10.9% on the MUES-SV2HS2S dataset. Full article
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15 pages, 5715 KiB  
Article
Crime Scene Shoeprint Retrieval Using Hybrid Features and Neighboring Images
by Yanjun Wu, Xinnian Wang and Tao Zhang
Information 2019, 10(2), 45; https://doi.org/10.3390/info10020045 - 30 Jan 2019
Cited by 18 | Viewed by 4325
Abstract
Given a query shoeprint image, shoeprint retrieval aims to retrieve the most similar shoeprints available from a large set of shoeprint images. Most of the existing approaches focus on designing single low-level features to highlight the most similar aspects of shoeprints, but their [...] Read more.
Given a query shoeprint image, shoeprint retrieval aims to retrieve the most similar shoeprints available from a large set of shoeprint images. Most of the existing approaches focus on designing single low-level features to highlight the most similar aspects of shoeprints, but their retrieval precision may vary dramatically with the quality and the content of the images. Therefore, in this paper, we proposed a shoeprint retrieval method to enhance the retrieval precision from two perspectives: (i) integrate the strengths of three kinds of low-level features to yield more satisfactory retrieval results; and (ii) enhance the traditional distance-based similarity by leveraging the information embedded in the neighboring shoeprints. The experiments were conducted on a crime scene shoeprint image dataset, that is, the MUES-SR10KS2S dataset. The proposed method achieved a competitive performance, and the cumulative match score for the proposed method exceeded 92.5% in the top 2% of the dataset, which was composed of 10,096 crime scene shoeprints. Full article
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17 pages, 35682 KiB  
Article
Depictions of Shoeprints in Northwest Portugal
by José Moreira and Ana M. S. Bettencourt
Heritage 2019, 2(1), 39-55; https://doi.org/10.3390/heritage2010004 - 24 Dec 2018
Cited by 1 | Viewed by 4851
Abstract
From the end of the 3rd millennium and the beginning of the 2nd millennium BCE, new motifs appear in Northwest Portugal. This corresponds to what one of the authors has called Figurative Art. The engravings of human feet—barefoot or with shoes—fall within this [...] Read more.
From the end of the 3rd millennium and the beginning of the 2nd millennium BCE, new motifs appear in Northwest Portugal. This corresponds to what one of the authors has called Figurative Art. The engravings of human feet—barefoot or with shoes—fall within this new “style”. This motif is not well known in Northern Portugal, although it has recently been the subject of a synthesis study on the Atlantic façade of this region. Starting from an inventory work, contextualising the several scales of analysis and the theoretical posture that knowledge is simultaneously cumulative and interpretative, this text reveals the shoeprints existing in Northwest Portugal and the interpretations that have been made about them. Currently there are 81 shoeprints in the region, distributed on 18 outcrops, in 17 different sites. This study has made it possible to create two typological subgroups, namely shoeprints with simple soles and with sole and heel. Within each group it was possible to perceive the existence of places with only one or few shoeprints, versus places with many shoeprints and that there are shoeprints of different dimensions and different orientations. The analysis of this data has made it possible to hypothesise that the engraving of these motifs may have arisen at the end of the Chalcolithic, beginning of the Bronze Age, reaching its peak during the latter period and ending at the beginning of the Iron Age. It is also hypothesised that they represent different age groups and that they may relate to pilgrimages or trips that formed part of rites of passage to adulthood, probably of individuals of higher status within a hierarchised society and which occurred at certain times of year, especially during the summer. Full article
(This article belongs to the Special Issue Heritage and Territory)
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12 pages, 1428 KiB  
Article
Biological Profile Estimation Based on Footprints and Shoeprints from Bracara Augusta Figlinae (Brick Workshops)
by Luís Miguel Marado and Jorge Ribeiro
Heritage 2018, 1(1), 33-44; https://doi.org/10.3390/heritage1010003 - 14 May 2018
Cited by 2 | Viewed by 5015
Abstract
Biological profile estimation is an important task of biological and forensic anthropologists. This includes sex, age, ancestry, and body morphology. In bioarchaeology, the biological profile is useful to analyze paleodemography, secular trends, paleopathology, and genetic processes, for example. Foot dimensions, footprints, and shoeprints [...] Read more.
Biological profile estimation is an important task of biological and forensic anthropologists. This includes sex, age, ancestry, and body morphology. In bioarchaeology, the biological profile is useful to analyze paleodemography, secular trends, paleopathology, and genetic processes, for example. Foot dimensions, footprints, and shoeprints can vary according to stature, age, sex, and body weight. The objective is to estimate these parameters in possible laterarii (brickworkers) from five footprints and seven shoeprints found in Roman bricks from Bracara Augusta. Estimation methods were applied to footprint and shoeprint measurements concerning foot length, foot breath, heel breadth, and length from heel to each finger. Three non-adult individuals were aged 1 to 4/5 years and were between 79.7 and 112.5 cm (±7.7 cm) tall. Five adults were likely female individuals, with statures between 144.2 and 159.9 cm. Methods were selected from samples preferably biologically similar to Portuguese people. This pioneer analysis provides biological insight on the Bracara Augusta laterarii and the population inhabiting Northwestern Iberia during Roman times. As a result of taphonomic constraints (cremation, soil acidity, and humidity), coeval osteological materials are hardly recovered, which further increases the relevance of this approach. Future research on methods based on Portuguese foot dimensions is essential. Full article
(This article belongs to the Special Issue Heritage and Territory)
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