# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 5 Anthropometric and Morphological Approaches for HRTF Personalisation
### Anthropometry Matching  
Anthropometry matching relies on a dataset where the available HRTFs are associated with corresponding anthropometric measures. When a new individual is considered, the method selects the HRTF in the dataset, minimising the suitably defined distance between the anthropometric measures of the subject and those in the dataset. In 2010, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Name of the Parameter** & **Symbol** & **Name of the Parameter** \\ \hline \(x_{1}\) & Head Width & \(x_{15}\) & Seated Height \\ \(x_{2}\) & Head Height & \(x_{16}\) & Head Circumference \\ \(x_{3}\) & Head Depth & \(x_{17}\) & Shoulder Circumference \\ \(x_{4}\) & Pinna Offset Down & \(d_{1}\) & Cavum Concha Height \\ \(x_{5}\) & Pinna Offset Back & \(d_{2}\) & Cymba Concha Height \\ \(x_{6}\) & Neck Width & \(d_{3}\) & Cavum Concha Width \\ \(x_{7}\) & Neck Height & \(d_{4}\) & Fossa Height \\ \(x_{8}\) & Neck Depth & \(d_{5}\) & Pinna Height \\ \(x_{9}\) & Torso Top Width & \(d_{6}\) & Pinna Width \\ \(x_{10}\) & Torso Top Width & \(d_{7}\) & Intertragonal Lisciure Width \\ \(x_{11}\) & Torso Top Depth & \(d_{8}\) & Cavum Conca Depth \\ \(x_{12}\) & Shoulder Width & \(\theta_{1}\) & Pinna Rotation Angle \\ \(x_{13}\) & Head Offset Forward & \(\theta_{2}\) & Pinna Are Angle \\ \(x_{14}\) & Height & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of anthropometric measures in the CIPIC dataset. Figure 4: Overview of methodologies for obtaining personalised HRTFs using anthropometric and morphological approaches. The arrows under ‘Regression’ represent the types of input data used: sparse measurements, 2D images, and 3D images. [PERSON] et al. [148] addressed the challenge of selecting individualised HRTFs for virtual audio environments using easily obtainable data, like photos of a listener's ear. By analysing a database of 46 measured HRTFs, the study identifies key morphological parameters through data reduction and data mining techniques. Using these parameters, it predicts a listener's preferred HRTF via linear regression, which is validated through listening tests. In 2015, [PERSON] et al. [149] introduced a novel approach utilising active shape models (ASMs) to automatically detect and extract key anthropometric features from 2D images, making it a more efficient solution compared to previous methods. The ASM is trained on a set of annotated images and is capable of identifying relevant anatomical features such as details of the ears, head, and torso from photographs of new subjects. The system begins by using computer vision to identify key anthropometric points on the subject's face and body. Once these points are located, the distances between them are measured and converted into metric units, generating a set of anthropometric parameters that describe the subject's unique anatomy. This information is then used to find the best HRTF match in the CIPIC HRTF database. In 2022, [PERSON] et al. [150] proposed a matching model based on a dataset of 3D ear meshes associated with landmark annotations and HRTFs. The authors used the Euclidean distance between the landmarks to create a ranking of distances from a selected individual and all the others in the dataset. Furthermore, they introduced different metrics to compare and rank the HRTFs of the selected user with all the others in the dataset: two objective measures, the spectral difference error (SDE) and the sagittal-plane localisation model (see Section 2.2), and a subjective measure based on the performances of real individuals in virtual reality sound detection task. The hypothesis is that the ear with the shortest distance from the ear of the selected individual corresponds also to the shortest distance between the corresponding HRTFs. In the same year (2022), [PERSON] et al. [151] proposed a streamlined method for personalising HRTFs using a convolutional neural network (CNN) that predicts ear landmarks from a single photo.