A Novel Face-to-Skull Prediction Based on Face-to-Back Head Relation

Skull structures are important for biomechanical head simulations, but they are mostly reconstructed from medical images. These reconstruction methods harm the human body and have a long processing time. Currently, skull structures can be straightforwardly predicted from the head, but a full head shape must be available. Most scanning devices can only capture the face shape. Consequently, a method that can quickly predict the full skull structures from the face is necessary. In this study, a novel face-to-skull prediction procedure is introduced. Given a three-dimensional (3-D) face shape, a skull mesh could be predicted so that its shape would statistically fit the face shape. Several prediction strategies were conducted. The optimal prediction strategy with its optimal hyperparameters was experimentally selected through a ten-fold cross-validation with 329 subjects. As a result, the face-to-skull prediction strategy based on the relations between face head shape and back head shape, between face head shape and face skull shape, and between back head shape and back skull shape was optimal. The optimal mean mesh-to-mesh distance (mean ± SD) between the predicted skull shapes and the ground truth skull shapes was 1.93 ± 0.36 mm, and those between the predicted skull meshes and the ground truth skull meshes were 2.65 ± 0.36 mm. Moreover, the prediction errors in back-skull and muscle attachment regions were 1.7432 ± 0.5217 mm and 1.7671 ± 0.3829 mm, respectively. These errors are within the acceptable range of facial muscle simulation. In perspective, this method will be employed in our clinical decision support system to enhance the accuracy of biomechanical head simulation based on a stereo fusion camera system. Moreover, we will also enhance the accuracy of the face-to-skull prediction by diversifying the dataset into more varied geographical regions and genders. More types of parameters, such as Body Mass Index (BMI), coupled with head-to-skull thicknesses, will be fused with the proposed face-to-skull procedure.

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Publication Details

Journal
Computers, materials & continua/Computers, materials & continua (Print)
Published
2025-01-01
DOI
https://doi.org/10.32604/cmc.2025.065279
Primary Topic
Face recognition and analysis
Type
article
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article

A Novel Face-to-Skull Prediction Based on Face-to-Back Head Relation

Tien Tuan Dao, Ngoc-Bich Le, Tan–Nhu Nguyen, Nhat-Minh Nguyen et al.
Computers, materials & continua/Computers, materials & continua (Print)
Face recognition and analysis
article

A Novel Face-to-Skull Prediction Based on Face-to-Back Head Relation

Tien Tuan Dao, Ngoc-Bich Le, Tan–Nhu Nguyen, Nhat-Minh Nguyen, Trong-Pham Nguyen-Huu, Lan-Nhi Tran-Ngoc, Khanh-Linh Dinh-Bui
article en

Abstract

Skull structures are important for biomechanical head simulations, but they are mostly reconstructed from medical images. These reconstruction methods harm the human body and have a long processing time. Currently, skull structures can be straightforwardly predicted from the head, but a full head shape must be available. Most scanning devices can only capture the face shape. Consequently, a method that can quickly predict the full skull structures from the face is necessary. In this study, a novel face-to-skull prediction procedure is introduced. Given a three-dimensional (3-D) face shape, a skull mesh could be predicted so that its shape would statistically fit the face shape. Several prediction strategies were conducted. The optimal prediction strategy with its optimal hyperparameters was experimentally selected through a ten-fold cross-validation with 329 subjects. As a result, the face-to-skull prediction strategy based on the relations between face head shape and back head shape, between face head shape and face skull shape, and between back head shape and back skull shape was optimal. The optimal mean mesh-to-mesh distance (mean ± SD) between the predicted skull shapes and the ground truth skull shapes was 1.93 ± 0.36 mm, and those between the predicted skull meshes and the ground truth skull meshes were 2.65 ± 0.36 mm. Moreover, the prediction errors in back-skull and muscle attachment regions were 1.7432 ± 0.5217 mm and 1.7671 ± 0.3829 mm, respectively. These errors are within the acceptable range of facial muscle simulation. In perspective, this method will be employed in our clinical decision support system to enhance the accuracy of biomechanical head simulation based on a stereo fusion camera system. Moreover, we will also enhance the accuracy of the face-to-skull prediction by diversifying the dataset into more varied geographical regions and genders. More types of parameters, such as Body Mass Index (BMI), coupled with head-to-skull thicknesses, will be fused with the proposed face-to-skull procedure.

Computers, materials & continua/Computers, materials & continua (Print)Vol. 84(2)
Openalex Percentile: Top 88%
Face recognition and analysis
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