Human bite marks – A computer-based analysis using adobe photoshop

Chintala, Lalitha; Marthala, Manjula; Goyal, Stuti; Chaitanya, V. Krishna; Hussain, K A.; Chaitanya, YC

doi:10.4103/jiaomr.jiaomr_87_17

Cited by 5 publications

(6 citation statements)

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“…Mac Donald defined bite mark as “a representative pattern left in an object or tissue by the dental structures, either alone or in combination with other oral structures of an animal or human” [4]. According to the Old Testament, Adam was convinced by Eve to place “bite marks” in apple.…”

Section: Introductionmentioning

confidence: 99%

“…These are encountered in cases of crimes especially homicides, quarrels, abduction, child abuse, and sexual assaults and during sports events and sometimes intentionally inflicted to frame someone fallaciously [8, 9]. The terms commonly used in bite mark analysis are victim—the recipient of the bite mark and perpetrator—the person who caused the bite mark [4]. While bite marks on the body are intentionally caused, those found on food articles are usually imperceptibly left by the offenders at the scene of the crime.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Computer-Assisted Method of Bite Mark Analysis for Gender Determination

Maji

Khaitan

Sinha

et al. 2018

Journal of Environmental and Public Health

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Background Bite mark analysis is an imperative area of forensic odontology and considered the commonest form of dental evidence presented in the criminal court. The process of comparing bite marks with a suspect's dentition includes analysis and measurement of shape, size, and position of an individual's teeth. The present study was aimed to evaluate the bite marks of males and females using a novel indirect computer-assisted method and explicate its application in forensic odontology. Materials and methods 60 subjects (30 males and 30 females) with normal occlusion were included in the present study. Bite registrations were obtained with help of modelling waxes, and positive replicas were prepared with dental stone and barium powder. Intraoral periapical radiographs were taken for the same. The radiographs obtained were scanned and analyzed by measuring tools using Sidexis Next Generation software. Intercanine distance (ICD), line AB, angle ABX, and angle ABY were measured. The Kruskal–Wallis test was performed to compare the bite marks of males and females. Results The mean ICD of males and females was found to be 32.95 mm and 29.84 mm, respectively, and was statistically highly significant with a p value <0.001. The mean ICD, line AB, and angle ABX were found to be higher in males when compared to females. Conclusion Analysis of bite marks using this novel computer-assisted method is a simple, reliable, easily reproducible, and economical technique with confidentiality of the identity of the participants involved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Computer-Assisted Method of Bite Mark Analysis for Gender Determination

Maji

Khaitan

Sinha

et al. 2018

Journal of Environmental and Public Health

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“…The usual term used in bite mark analysis is the victim, which indicates the recipient of the bite mark, and the perpetrator is the person who caused the bite marks (Chintala et al, 2018 ). Unlike bite marks on the body, which are usually caused on purpose, the offenders inadvertently leave bite marks on food at the crime scene.…”

Section: Discussionmentioning

confidence: 99%

Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review

Mohammad

Ahmad

Kurniawan

et al. 2022

Front. Artif. Intell.

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BackgroundForensic odontology may require a visual or clinical method during identification. Sometimes it may require forensic experts to refer to the existing technique to identify individuals, for example, by using the atlas to estimate the dental age. However, the existing technology can be a complicated procedure for a large-scale incident requiring a more significant number of forensic identifications, particularly during mass disasters. This has driven many experts to perform automation in their current practice to improve efficiency.ObjectiveThis article aims to evaluate current artificial intelligence applications and discuss their performance concerning the algorithm architecture used in forensic odontology.MethodsThis study summarizes the findings of 28 research papers published between 2010 and June 2022 using the Arksey and O'Malley framework, updated by the Joanna Briggs Institute Framework for Scoping Reviews methodology, highlighting the research trend of artificial intelligence technology in forensic odontology. In addition, a literature search was conducted on Web of Science (WoS), Scopus, Google Scholar, and PubMed, and the results were evaluated based on their content and significance.ResultsThe potential application of artificial intelligence technology in forensic odontology can be categorized into four: (1) human bite marks, (2) sex determination, (3) age estimation, and (4) dental comparison. This powerful tool can solve humanity's problems by giving an adequate number of datasets, the appropriate implementation of algorithm architecture, and the proper assignment of hyperparameters that enable the model to perform the prediction at a very high level of performance.ConclusionThe reviewed articles demonstrate that machine learning techniques are reliable for studies involving continuous features such as morphometric parameters. However, machine learning models do not strictly require large training datasets to produce promising results. In contrast, deep learning enables the processing of unstructured data, such as medical images, which require large volumes of data. Occasionally, transfer learning was used to overcome the limitation of data. In the meantime, this method's capacity to automatically learn task-specific feature representations has made it a significant success in forensic odontology.

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“…In the past, dental identification predates finger printing, the usage of bite mark analysis is emerging to be recognized and procedure is in the development process. Similar to fingerprint, the bite mark made by the teeth could be a tool for Detection [4].…”

Section: Introductionmentioning

confidence: 99%

“…By using the visual interpretation of tooth-mark problems, the study explored the effects of receptive field size on the classification result. Chintala et al [12] performed detection of human bite marks on victim and analyzed with computer-based superimposition technology through Adobe Photoshop software. Five bite marks cases have been investigated in the literature.…”

Section: Introductionmentioning

confidence: 99%

Intelligent bite marking analysis and classification using deep convolutional neural network based Xception model

Chandramouleeswaran

Puviarasan

2022

ijhs

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Bite mark analysis and classification play a vital role in forensics. The recent advances in computer vision and deep learning models paves a way for the design of automated bite mark detection and classification process. This article focuses on the design of intelligent bite marking analysis and classification using deep convolutional neural network based Xception model. The major goal of the proposed model is to determine the appropriate class labels for the bite marked images. The proposed model initially intends to pre-process the bite marked images in different ways such as hair removal, median filtering based noise removal, and adaptive histogram based contrast enhancement. Besides, Chan Vese Segmentation approach is applied for segmenting the bite marked images. The data augmentation process is performed for increasing the count of images. In addition, Xception model is employed for the extraction of features. Finally, two machine learning (ML) classifications such as support vector machine (SVM) and logistic regression (LR) models are employed for image classification. For demonstrating the enhanced performance of the presented models, a set of simulations were carried out on their own dataset and the results ensured the betterment of the proposed model over the other existing models.

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Human bite marks – A computer-based analysis using adobe photoshop

Cited by 5 publications

References 0 publications

A Novel Computer-Assisted Method of Bite Mark Analysis for Gender Determination

A Novel Computer-Assisted Method of Bite Mark Analysis for Gender Determination

Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review

Intelligent bite marking analysis and classification using deep convolutional neural network based Xception model

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