2025 Munich Tech Arena
Media Technology
Anthropometric Data Extraction
The goal of this challenge is to extract anthropometric data [1], specifically head and ear parameters, from a series of RGBD images of a human subject. As illustrated in Fig. 1, submitted models are expected to accept a fixed number of subject images captured from predefined camera positions and output precise anthropometric measurements of the human head and ears, with particular emphasis on the pinnas.
Fig.1: Block diagram of the anthropometric feature extraction process
Provided input:
A dataset consisting of subject images along with corresponding anthropometric measurements is provided.
The subject images are captured from horizontal camera positions evenly spaced at 5-degree intervals around each subject, resulting in 72 images per subject. These RGBD images are supplied in HEIC format [2].
For evaluation purposes, the images will be organized into three different input sets:
Expected output:
Participants must provide anthropometric measurements of both the head and the pinnas.
The submitted model should be able to handle each of the three input configurations described in the preceding section.
The model will be evaluated on an undisclosed set of subjects. For each subject, the submitted model will be evaluated on 3 different sets of input images, as described in the above section. The output anthropometric vector will be compared against the ground truth anthropometric vector using the Euclidean distance:
In this equation, Sout denotes the model output anthropometric vector, Sgt the ground truth anthropometric vector, and N the number of feature dimensions.
Notably, since the evaluation is based on Euclidean distance, lower values indicate higher accuracy and therefore better performance. A reference score, computed using the average anthropometric measurements across all subjects in the dataset, serves as a lower bound for performance. While this reference does not represent an optimal solution, it provides a baseline that submitted systems are expected to surpass in order to be considered valid.
Reference:
[1] https://en.wikipedia.org/wiki/Anthropometry
[2] https://en.wikipedia.org/wiki/High_Efficiency_Image_File_Format
NDA Requirement
Teams that choose to participate in the Anthropometric Data Extraction track are required to submit a signed Non-Disclosure Agreement (NDA) in order to receive access to the project datasets.
Please read the following requirements and follow the instructions carefully:
Key Requirements
Step 1: Download the NDA Template through download link
Step 2: Sign the NDA
-The NDA may be printed, signed, and scanned, or signed electronically.
-Each team member must sign the NDA individually.
Step 3: Upload the Signed NDA and Provide Your Team Email
Dataset access will only be granted after the NDA has been successfully submitted and verified by the organizing committee and Imperial College London.
After submitting the signed NDA on the platform, it typically takes up to one week to receive the dataset link. The Imperial College London team will send dataset access emails every Wednesday to the email addresses provided. Please plan your time accordingly.
Two-Part Evaluation Structure
The competition consists of two evaluation parts:
All teams are required to submit a written proposal outlining their solution approach by September 15, 2025.
👉 [Click here to view the idea template]
Submissions will be reviewed by our judging panel to ensure they meet the template and content requirements.
If your submission is approved, your team will receive full marks for Part 1, which accounts for 20% of the final score.
This part contributes 80% of the total score.