Affiliations: [a] Computational Cognitive Sciences Lab, Center for Computing Research, Instituto Politécnico Nacional, Mexico City, Mexico
| [b] Biomedical Informatics Group (GIB), Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, Madrid, Spain
Correspondence:
[*]
Corresponding author. Hiram Calvo, Computational Cognitive Sciences Lab, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07700, Mexico. E-mail: [email protected].
Note: [1] This work was supported by the Instituto Politécnico Nacional (COFAA, SIPIPN, Grant SIP 20240610) and the Mexican Government (CONAHCyT, SNI).
Abstract: Inferring phylogenetic trees in human populations is a challenging task that has traditionally relied on genetic, linguistic, and geographic data. In this study, we explore the application of Deep Learning and facial embeddings for phylogenetic tree inference based solely on facial features. We use pre-trained ConvNets as image encoders to extract facial embeddings and apply hierarchical clustering algorithms to construct phylogenetic trees. Our methodology differs from previous approaches in that it does not rely on preconstructed phylogenetic trees, allowing for an independent assessment of the potential of facial embeddings to capture relationships between populations. We have evaluated our method with a dataset of 30 ethnic classes, obtained by web scraping and manual curation. Our results indicate that facial embeddings can capture phenotypic similarities between closely related populations; however, problems arise in cases of convergent evolution, leading to misclassifications of certain ethnic groups. We compare the performance of different models and algorithms, finding that using the model with ResNet50 backbone and the face recognition module yields the best overall results. Our results show the limitations of using only facial features to accurately infer a phylogenetic tree and highlight the need to integrate additional sources of information to improve the robustness of population classification.
Keywords: Convolutional neural networks, deep learning, hierarchical clustering, phylogenetic tree
