In a world where the need for accurate tools to identify, the human face has emerged as one of the most important biometric features that carry a unique genetic imprint.
Facial features have always been a visual key to identify individuals, not only in daily life, but in accurate fields such as criminal evidence, where formal characteristics play a decisive role in identifying identity, especially when the absence of traditional identification means.
With the development of highly productive genetic sequence techniques, criminal recognition prospects expanded to include a more ambitious approach: extracting facial features from DNA alone.
Despite the challenges imposed by the complex nature of facial genetics, and the intertwined effects of environmental and genetic factors, attempts to embody human features are followed by genetic material.
However, converting the genetic code directly into realistic facial features remained a complex goal, due to the limited genetic understanding and the difficulty of accurate prediction in the form of the human face. But the emergence of artificial intelligence techniques, especially multimedia obstetric models, give way to radical innovations in this field.
As part of this context, Divace is highlighted as a new approach that employs the prevalence and maps of the correspondence between genetic data and images, to rebuild the human face three -dimensional based on the DNA sequence.
How does “Devis” turn the genetic code into a three -dimensional face?
In a recent study published in the magazine “Advanced Science”, a team of researchers at the Chinese Academy of Sciences has developed an innovative model called “Divis”, based on artificial intelligence techniques to rebuild 3D images of human faces directly from DNA data.
This model links specific genetic variables known as Single Nucleotide polymorphisms-nps and visible facial features, which enables it to accurately expect facial features.
In a comment, Lunin Chen, one of the authors of the study, said: “Divis’ was amazingly able to create three -dimensional images of individuals, depending on only the DNA data, expecting their appearance at different future ages.”
“Device” uses a mixture of advanced techniques, including multi -media learning, hybrid structures of transformes, spiral conversion (Spiral Convolutions), in addition to a generation mechanism that depends on the Diffrance Models, to translate the genetic code into a high realistic face representation Accuracy.
Amazing genetic accuracy in simulating features
According to the search paper, the “Divis” model was trained using 9674 data collection that includes the SNPS genetic variables and 3D scannings of the faces of individuals from the Chinese Han.
Where the model depends on the representation of genetic data and facial surfaces in a distinctive low -dimensional space through variation learning techniques, then he uses obstetric ingredients based on the prevalence model to rebuild the 3D face of this representation.
Including additional variables such as age, gender, and BMI (BMI) has significantly enhancing the accuracy of rebuilding, as the “Divis” model achieved a first-class definition rate (RNK-1) by 3.33%, and an under curve (AUC) by 80.7% in verification tasks.
On the other hand, the average error of rebuilding the face (Euclidean distance) reached 3.52 millimeters when only the SNBS variables were used, and it decreased to 2.93 millimeters when the virtual variables are included. The model also showed an accurate capacity to capture facial changes across different age groups, and achieved high classification accuracy in distinguishing between facial features.
Is it possible to trust the results of “Devis” despite the limited data?
Although the Divis model exceeded the maintenance of high accuracy even when using partial genetic data, a decline in performance was observed when the coverage of the genetic variables “SNBS” decreased to less than 70%.
However, the model showed a semi -identical face diversity, with DPP diversity 9.66 compared to 0.9999 in real images.
Among its most prominent advantages are its ability to explain. Thanks to the use of SHAP and GWAS, researchers were able to identify the genetic variables of SNBS with close association with specific features such as the shape of the nose and the structure of the cheekbones. These results were biologically verified, as it showed a compatibility with the developmental paths known as the “Gene Oontology”.
Wide application prospects and a promising future
Devis shows multiple applications in various fields, the most prominent of which are:
Forensic medicine: It can be used to get to know individuals depending on the damaged or incomplete “DNA” samples. In personal medicine: It contributes to providing visions about the role of genes in shaping facial features. In genetic research: enhances the understanding of the relationship between Genome and formal qualities.
In a field experience, the participants managed to match the real faces with the structural faces with a resolution of 75.6% in multiple options tests of 5 pictures.
Although the model was developed using genetic data from an ethnic homogeneous population (Chinese race), its structure is generalized. The researchers stressed the need to train in the future on multi -ethnic data to expand its use, and to ensure justice and fairness in various applications.
Ethical ethical questions
Despite the remarkable technical progress of Divis, its use raises fundamental questions related to genominal privacy, the possibility of re -identification of individuals, and the employment of technology in biometric monitoring systems.
As the ability to generate features of anonymous genetic data open the door to the risk of abuse in areas such as law, health care, and insurance companies.
Experts have warned that misusing the ability to infer the apparent pattern of DNA may lead to unauthorized access to sensitive personal data, or even a discrimination based on expected genetic features.
For example, although the virtual criminal photography “FDB” has become an advanced tool used in complex issues, it still lacks sufficient accuracy regarding the anticipation of facial features, which may lead to wrong suspicion in innocent people or specific population groups.
This anxiety was evident in an incident in 2022 in Canada, when the Edmantton city police asked the public to help identify a suspect using a computer -born image based on genetic data from DNA, in a sexual assault case.
The move faced severe criticism for the weak transparency in the method of generating the image, which later prompted the police to withdraw its request. The photo was produced by Parabon Nanolabs, which was subjected to repeated criticism by privacy and ethics activists.
For his part, co -researcher Chen confirmed to the “CORTHOUSE SERVICE” (Email “that the moral side was not marginal, but rather an” integral part of the research process “, noting that the team took from the beginning a number of positive measures to ensure moral and legal compliance, including work according to the criteria of moral institutional review, and restricting the use of use Genetic data within the limits of scientific research.
However, the matter remains dependent on the development of a comprehensive legislative and moral framework, and the opening of a multidisciplinary dialogue that combines scholars, legislators, and human rights experts, to ensure that these innovations are directed within safe and fair paths.
In the end, despite the advanced accuracy he has reached, the “Divis” model is still in the stage of experience and exploration, which requires the expansion of the ethnic database, and improving the representation of human diversity, as well as strengthening the moral and legal frameworks associated with it.
Perhaps the most prominent question in the future will not only: “Is it possible to predict the human face of his DNA?”, But: “How can we use this ability with responsibility?” A question that puts artificial intelligence and genetic science in front of a double test: technology and values test at the same time.