Comparative Study of Three Different Methods of Overlay Generation in Bite Mark Analysis

Daniel, Mariappan Jonathan; Bhardwaj, Nupur; Srinivasan, S.V.; Jimsha, VK; Marak, Fremingston K.

doi:10.5958/0974-0848.2015.00005.6

Cited by 3 publications

(4 citation statements)

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“…Uniqueness is replicated on the bitten substrate in sufficient detail to enable a match to the individual under suspicion [8, 17]. Daniel et al analyzed the bite marks using cheese, apple, and chocolate and found that the accuracy was greater for chocolate and cheese [1]. Patil et al and Kashyap et al recorded and determined bite marks using modelling wax which was also used in the present study [9, 13].…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

“…“The criminal may lie through his teeth though teeth themselves cannot lie” is rightly said by Furness. Anything diverse, such as discrepancy from normality, becomes an important tool when trying to establish an identity of a suspect [1, 2]. …”

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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“…These outcomes showed potential of using arch size variables for gender prediction which require further investigations of their applicability in forensic services. However, reports on gender prediction models complete with error rates were lacking (6,7,16,17).…”

Section: Introductionmentioning

confidence: 99%

Gender Determination in Pakhtun Pakistani Population Using Dental Arch Dimensions: A Digital Model Study

Patrick¹,

Khamis²,

Zaim³

et al. 2023

MJMHS

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Introduction: In circumstances where the ante mortem list is unknown, gender determination would exclude one-half of the population, aid in a more precise search of the ante mortem records. This study aims to formulate gender prediction models in the Pakhtun Pakistani population using digital dental arch dimensions. Methods: Data collection and analysis of the dental casts were conducted on 128 subjects, 64 males and 64 females from the Pakistani population. The mean age of the subjects was 19.2 years old. Several linear dental arch dimensions were measured and recorded for both upper and lower arches. Results: It was found that gender differences in linear arch dimensions were statistically significant for both males and females (p<0.05); in which the arch dimensions for the males were larger than the arch dimensions for the females. Stepwise discriminant function analysis found that the highest discriminant power of the variables was present within the inter-second premolar width for the upper arch and inter-molar width for the lower arch. These variables significantly contributed to gender variance. Moreover, the prediction of 67.2% of original grouped cases for the upper arch and 66.4% of cross-validated group cases was correct. Similarly, the correct prediction was made on 64.8% of original grouped cases for the lower arch and 64.1% of cross-validated group cases. Conclusion: The dental arch dimensions were larger among the males compared to the females. Prediction models obtained in this study were moderately strong predictors which may be used as an adjunct to predict gender.

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“…Also, it can be found in inanimate objects or food substances at the crime scene. Identification of crime is complicated and requires pain-staking effort by several agencies [3]. Advancements in forensic sciences have made crime detection scientifically possible.…”

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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Comparative Study of Three Different Methods of Overlay Generation in Bite Mark Analysis

Cited by 3 publications

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

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

Gender Determination in Pakhtun Pakistani Population Using Dental Arch Dimensions: A Digital Model Study

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

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