Details

Autor(en) / Beteiligte

• Bartol, Kristijan
• Bojanić, David
• Petković, Tomislav
• Peharec, Stanislav
• Pribanić, Tomislav

Titel

Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement

Ist Teil von

• Sensors (Basel, Switzerland), 2022-02, Vol.22 (5), p.1885

Ort / Verlag

Switzerland: MDPI AG

Erscheinungsjahr

2022

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art approaches for body measurement from point clouds and images, demonstrate the comparable performance with the best methods, and even outperform several deep learning models on public datasets. The simplicity of the proposed regression model makes it perfectly suitable as a baseline in addition to the convenience for applications such as the virtual try-on. To improve the repeatability of the results of our baseline and the competing methods, we provide guidelines toward standardized body measurement estimation.