Title: A compact mathematical representation of human body silhouettes from frontal and lateral views

Authors: Fozia Rajbdad; Murtaza Aslam; Shoaib Azmat; Faiza Rajeb Dad; Jian Xu

Addresses: Electrical and Computer Engineering Department, Louisiana State University, Baton Rouge, LA 70803, USA ' Electrical and Computer Engineering Department, Louisiana State University, Baton Rouge, LA 70803, USA ' Electrical Engineering Department, COMSATS University Islamabad, University Road, 22060, Pakistan ' Electrical Engineering Department, Agriculture and Mechanical College, Southern University, 801 Harding Blvd Baton Rouge, LA 70807, USA ' Electrical and Computer Engineering Department, Louisiana State University, Baton Rouge, LA 70803, USA

Abstract: Human body silhouettes are used extensively in three-dimensional body shape modelling, activity recognition, apparel design, obesity, and posture assessment. These applications require efficient storage of human body images for future use and comparison. We proposed a novel one-dimensional mathematical representation of human body silhouettes from frontal and lateral views using a discrete cosine transform. Our method saved 75% of the storage space, significantly reducing costs, and achieved a compression ratio of 4:1 with an average reconstruction accuracy of 90% for all views of male and female images. Additionally, segment-wise silhouette representation decreased the average reconstruction complexity four times. Human body silhouettes are also modelled for the first time using polynomial curve fitting, discrete wavelet transform, and discrete Fourier transform with a systematic comparison. The polynomial curve fitting achieved the highest average space saving of 84%; however, reconstruction accuracy decreased by 12% compared to the discrete cosine transform. In addition, our novel method attained 46% additional storage space saving compared to standard two-dimensional JPEG and PNG image compression methods. Our work can be used to assess human body fat distribution, detect pose abnormalities and classify body shapes, ages, and genders.

Keywords: silhouettes; frontal; lateral; mathematical representation; reconstruction; discrete cosine transform; DCT.

DOI: 10.1504/IJBET.2024.138708

International Journal of Biomedical Engineering and Technology, 2024 Vol.45 No.2, pp.102 - 128

Received: 15 Dec 2022
Accepted: 04 Jun 2023
Published online: 29 May 2024 *

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