Computer-aided superimposition via reconstructing and matching 3D faces to 3D skulls for forensic craniofacial identifications

Tan, Joo Shun; Liao, Iman Yi; Venkat, Ibrahim; Belaton, Bahari; Jayaprakash, Paul T.

doi:10.1007/s00371-019-01767-7

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…For many forensic experts, the current benefits of advanced AI processes are still unknown. For example, automated AI algorithms for skull damage detection from CT [91] or soft-tissue prediction of a face from the skull [66,67,89,92] are still a mystery to many outstanding forensic scientists. Enabling them would catapult forensic research to a new era [1].…”

Section: Basic Terminology and Principles In Era Of Ai Enhanced Forensic Medicinementioning

confidence: 99%

“…Prediction of soft tissues according to the hard tissues of the skull and vice versa can be significantly improved upon big-data training of 3D CNN with supplementary metadata about age, sex, BMI or ethnicity. New algorithms to perform facial reconstruction from a given skull has forensic application in helping the identification of skeletal remains when additional information is unavailable [29,64,[66][67][68][69][70]72,73,85,86,88,89,92,137]. Implementation of 3D CNN can also unintentionally open pandora box of guided improving the morphology of the facial soft-tissues.…”

Section: Artificial Intelligence Implementation In Soft-tissue Face Prediction From Skull and Vice Versamentioning

confidence: 99%

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Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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Section: Basic Terminology and Principles In Era Of Ai Enhanced Forensic Medicinementioning

confidence: 99%

Section: Artificial Intelligence Implementation In Soft-tissue Face Prediction From Skull and Vice Versamentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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“…To many forensic experts, current benefits of advanced AI processes are still unknown. For example, automated AI algorithms for skull damage detection from CT [95] or soft-tissue prediction of a face from skull [69,70,93,96] are still a mystery to many remarkable forensic scientists. Enabling them would catapult forensic research to a new era [1].…”

Section: Basic Terminology and Principles In Era Of Ai Enhanced Forensic Medicinementioning

confidence: 99%

“…Prediction of soft tissues according to the hard tissues of the skull and vice versa can be significantly improved upon big-data training of 3D CNN with supplementary metadata about age, sex, BMI or ethnicity. New algorithms to perform facial reconstruction from a given skull has forensic application in helping the identification of skeletal remains when other information is unavailable [62,67,[69][70][71][72][73]75,76,89,90,92,93,96,145]. Implementation of 3D CNN can also unintentionally open pandora box of guided improving the morphology of the facial soft-tissues.…”

Section: Artificial Intelligence Implementation In Soft-tissue Face Prediction From Skull and Vice Versamentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine

Thurzo¹,

Kosnáčová²,

Kurilová³

et al. 2021

Preprint

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Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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“…In [186] another 3D-3D superimposition approach was presented in an effort to contribute to computer-aided CFS. The proposed method emphasizes adherence to two important parameters: (1) maintaining the life-size of the face image in relation to the size of the skull, and (2) orienting the skull on an anthropological basis using selected feature points.…”

Section: D-3d Computer-aided Approaches For Craniofacial Superimpositionmentioning

confidence: 99%

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

et al. 2020

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This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.

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Computer-aided superimposition via reconstructing and matching 3D faces to 3D skulls for forensic craniofacial identifications

Cited by 9 publications

References 38 publications

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Use of Advanced Artificial Intelligence in Forensic Medicine

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

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