Estimation of 2D to 3D dimensions and proportionality indices for facial examination

Head poses and grimaces: Challenges for automated face identification algorithms?

In today's biometric and commercial settings, state-of-the-art image processing relies solely on artificial intelligence and machine learning which provides a high level of accuracy. However, these principles are deeply rooted in abstract, complex "black-box systems". When applied to forensic image identification, concerns about transparency and accountability emerge. This study explores the impact of two challenging factors in automated facial identification: facial expressions and head poses. The sample comprised 3D faces with nine prototype expressions, collected from 41 participants (13 males, 28 females) of European descent aged 19.96 to 50.89 years. Pre-processing involved converting 3D models to 2D color images (256 × 256 px). Probes included a set of 9 images per individual with head poses varying by 5° in both left-to-right (yaw) and up-and-down (pitch) directions for neutral expressions. A second set of 3,610 images per individual covered viewpoints in 5° increments from -45° to 45° for head movements and different facial expressions, forming the targets. Pair-wise comparisons using ArcFace, a state-of-the-art face identification algorithm yielded 54,615,690 dissimilarity scores. Results indicate that minor head deviations in probes have minimal impact. However, the performance diminished as targets deviated from the frontal position. Right-to-left movements were less influential than up and down, with downward pitch showing less impact than upward movements. The lowest accuracy was for upward pitch at 45°. Dissimilarity scores were consistently higher for males than for females across all studied factors. The performance particularly diverged in upward movements, starting at 15°. Among tested facial expressions, happiness and contempt performed best, while disgust exhibited the lowest AUC values.

Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial intelligence can see by Grad-CAM

OBJECTIVES

Aesthetic improvement is a significant concern in dental therapy. While orthodontic treatment primarily targets hard tissue, the impact on soft tissue and the extent of these changes remains empirical. This study aims to unveil the intricate relationship between facial soft tissue and skeletal types using artificial intelligence (AI) analysis.

METHODS

First, we collected a dataset of 1044 3-side-photographs and categorized them based on cephalometric measurements. After pre-processing and data augmentation, samples were fed to two independent models (Sfa, Res model) for training and testing. After validating that the Sfa model could accurately recognize the skeletal types based merely on photographs, Grad-CAM algorithm was utilized for model decipherment. Verification of the vital traits were carried out by facial adjustment simulation.

RESULTS

The Sfa model demonstrated superior accuracy (0.9293) in identifying skeletal types based solely on soft tissue, compared to the Res model (0.8395) and even trained orthodontists (0.764), testifying our hypothesis that AI could be more capable of processing imperceptible cues compared to mankind. Intriguingly, Grad-CAM revealed that cheek volume, forehead, chin and nasolabial traits could be representative features of each type, exceeding the traditional knowledge which merely concerns mandible and chin.

CONCLUSION

By constructing a deep learning model as a classifier and then decipher it with Grad-CAM, we revealed the subtle and unnoticed cues associating skeletal and soft tissue, as well as provided a novel approach that could aid practitioners in devising tailored treatment plans for enhanced esthetic outcomes.

CLINICAL SIGNIFICANCE

The proposed AI methods offer valuable assistance to practitioners in identifying uncoordinated facial traits that may detract from a patient's attractiveness. By incorporating these insights into customized treatment plans, dental therapy can maximize esthetic benefits for individual patient.

Can a spontaneous smile invalidate facial identification by photo-anthropometry?

Purpose

Using images in the facial image comparison process poses a challenge for forensic experts due to limitations such as the presence of facial expressions. The aims of this study were to analyze how morphometric changes in the face during a spontaneous smile influence the facial image comparison process and to evaluate the reproducibility of measurements obtained by digital stereophotogrammetry in these situations.

Materials and Methods

Three examiners used digital stereophotogrammetry to obtain 3-dimensional images of the faces of 10 female participants (aged between 23 and 45 years). Photographs of the participants' faces were captured with their faces at rest (group 1) and with a spontaneous smile (group 2), resulting in a total of 60 3-dimensional images. The digital stereophotogrammetry device obtained the images with a 3.5-ms capture time, which prevented undesirable movements of the participants. Linear measurements between facial landmarks were made, in units of millimeters, and the data were subjected to multivariate and univariate statistical analyses using Pirouette® version 4.5 (InfoMetrix Inc., Woodinville, WA, USA) and Microsoft Excel® (Microsoft Corp., Redmond, WA, USA), respectively.

Results

The measurements that most strongly influenced the separation of the groups were related to the labial/buccal region. In general, the data showed low standard deviations, which differed by less than 10% from the measured mean values, demonstrating that the digital stereophotogrammetry technique was reproducible.

Conclusion

The impact of spontaneous smiles on the facial image comparison process should be considered, and digital stereophotogrammetry provided good reproducibility.

Craniofacial photographic superimposition: New developments

Craniofacial superimposition is a technique used in the field of forensic anthropology to assist in the analysis of an unknown skull. The process involves superimposing an image of the recovered skull over an ante mortem image of the suspected individual. In the past two decades, there has been a decline in the application due to the development of molecular analysis as a more precise and accurate identification technique. Despite its decrease in use, there has been significant development in superimposition techniques in the past five years, specifically to standardize procedures. One project, MEPROCS (The New Methodologies and Protocols of Forensic Identification by Craniofacial Superimposition), has attempted to establish a framework for solving the problems of past superimposition techniques. Future researchers should consider integrating information gleaned from clinical practices with the statistical and technical advances of craniofacial superimposition for better facilitating its use in forensic anthropology.