The unfamiliar face effect on forensic craniofacial reconstruction and recognition

Lee, Won Joon; Wilkinson, Caroline

doi:10.1016/j.forsciint.2016.11.003

Cited by 9 publications

(4 citation statements)

References 68 publications

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“…Because the geometry of 3D faces is substantially identical, many of the similarity assessment methods for 3D objects are not applicable to evaluating 3D faces. To date, most scholars have evaluated craniofacial reconstruction results using subjective methods [2–7] to evaluate craniofacial similarity by collecting a certain number of tests and designing different evaluation strategies. Although this type of evaluation method is consistent with human cognitive theory, it requires a great deal of manpower and time, and the accuracy of the evaluation results is influenced by subjective human factors.…”

Section: Related Workmentioning

confidence: 99%

Craniofacial similarity analysis through sparse principal component analysis

et al. 2017

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The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data. Compared with principal component analysis (PCA), SPCA can effectively reduce the dimensionality and simultaneously produce sparse principal components with sparse loadings, thus making it easy to explain the results. The experimental results indicated that the evaluation results of PCA and SPCA are consistent to a large extent. To compare the inconsistent results, we performed a subjective test, which indicated that the result of SPCA is superior to that of PCA. Most importantly, SPCA can not only compare the similarity of two craniofacial datasets but also locate regions of high similarity, which is important for improving the craniofacial reconstruction effect. In addition, the areas or features that are important for craniofacial similarity measurements can be determined from a large amount of data. We conclude that the craniofacial contour is the most important factor in craniofacial similarity evaluation. This conclusion is consistent with the conclusions of psychological experiments on face recognition and our subjective test. The results may provide important guidance for three- or two-dimensional face similarity evaluation, analysis and face recognition.

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Section: Related Workmentioning

confidence: 99%

Craniofacial similarity analysis through sparse principal component analysis

et al. 2017

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“…This could, in part, account for the weak face matching results obtained in our study, and in previous studies. [41][42][43]…”

Section: Tested 3-d Facial Reconstructions Usingmentioning

confidence: 99%

The development and testing of Thai facial soft tissue thickness data in three-dimensional computerized forensic facial reconstruction

et al. 2021

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Forensic facial reconstruction is a useful tool to assist the public in recognizing human remains, leading to positive forensic investigation outcomes. To reproduce a virtual face, facial soft tissue thickness is one of the major guidelines to reach the accuracy and reliability for three-dimensional computerized facial reconstruction, a method that is making a significant contribution to improving forensic investigation and identification. This study aimed to develop a facial soft tissue thickness dataset for a Thai population, and test its reliability in the context of facial reconstruction. Three-dimensional facial reconstruction was conducted on four skulls (2 males and 2 females, with ages ranging between 51 to 60 years). Two main tools of three-dimensional computer animation and modeling software—Blender and Autodesk Maya—were used to rebuild the three-dimensional virtual face. The three-dimensional coordinate ( x, y, z) cutaneous landmarks on the mesh templates were aligned homologous to the facial soft tissue thickness markers on the three-dimensional skull model. The final three-dimensional virtual face was compared to the target frontal photograph using face pool comparison. Four three-dimensional virtual faces were matched at low to moderate levels, ranging from 30% to 70% accuracy. These results demonstrate that the facial soft tissue thickness database of a Thai population applied in this study could be useful for three-dimensional computerized facial reconstruction purposes.

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“…Computer-generated 3D CFRs can now be performed (Wilkinson et al 2006). Using appropriate software, CFR can be achieved using automated (Imaizumi et al 2019;Gietzen et al 2019) or model-based (i.e., virtual sculpture) methods (Wilkinson et al 2006;Lee and Wilkinson 2016). The accuracy of computer-generated CFRs can be evaluated through 3D geometric surface analysis, wherein the 3D CFRs are aligned in virtual space (Lee et al 2014).…”

Section: Testing the Accuracy Of The Mummy Cfrmentioning

confidence: 99%