Abstract
Abstract. The problem of facial appearance reconstruction (or facial approximation) basing on a skull is very important as for anthropology and archaeology as for forensics. Recent progress in optical 3D measurements allowed to substitute manual facial reconstruction techniques with computer-aided ones based on digital skull 3D models. Growing amount of data and developing methods for data processing provide a background for creating fully automated technique of face approximation.The performed study addressed to a problem of facial approximation based on skull digital 3D model with deep learning techniques. The skull 3D models used for appearance reconstruction are generated by the original photogrammetric system in automated mode. These 3D models are then used as input for the algorithm for face appearance reconstruction. The paper presents a deep learning approach for facial approximation basing on a skull. It exploits the generative adversarial learning for transition data from one modality (skull) to another modality (face) using digital skull 3D models and face 3D models. A special dataset containing skull 3D models and face 3D models has been collected and adapted for convolutional neural network training and testing. Evaluation results on testing part of the dataset demonstrates high potential of the developed approach in facial approximation.
Highlights
Craniofacial reconstruction tries to solve a problem of forecasting a person appearance having only a skull. This problem is important for various application areas such as forensics, archaeology, anthropology
Such a procedure was some kind of art, so it resulted in subjective appearance reconstruction depending on the author’s foresight
The main contributions of the paper are (1) the pipeline for facial approximation based on machine learning, (2) the annotated dataset for generative adversarial network (GAN) training for skull a 3D model translation to the corresponding face 3D model, (3) the evaluation of the proposed GAN model performance for facial approximation
Summary
Craniofacial reconstruction (or facial approximation) tries to solve a problem of forecasting a person appearance having only a skull. First techniques for facial approximation operated with real skull (or its gypsum copy) and a clay, used by an expert-anthropologist for creating a sculpture forecast of the unknown face. Such a procedure was some kind of art, so it resulted in subjective appearance reconstruction depending on the author’s foresight. Some studies propose the automated methods for facial approximation based on statistical deformable shape models for skull and face morphology. Another great tendency of nowadays is incorporating machine learning techniques in wide variety of applications. The main contributions of the paper are (1) the pipeline for facial approximation based on machine learning, (2) the annotated dataset for generative adversarial network (GAN) training for skull a 3D model translation to the corresponding face 3D model, (3) the evaluation of the proposed GAN model performance for facial approximation
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