Exploring Body Texture from mmW Images for Person Recognition

TL;DR

This paper investigates the use of millimeter wave (mmW) texture information from different body parts for person recognition, demonstrating that mmW torso features are highly discriminative and proposing a novel CNN fusion method to improve accuracy.

Contribution

It introduces a comprehensive analysis of mmW texture features from face, torso, and whole body for recognition, and proposes a new CNN-based fusion technique to enhance performance.

Findings

mmW torso region is most discriminative

CNN features outperform hand-crafted features on face and whole body

Fusion technique achieves 2% EER and 99% rank-1 accuracy

Abstract

Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this work we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained Convolutional Neural Networks (CNN) models. First, we analyze the individual performance of three mmW body parts, concluding that: i) mmW torso region is more discriminative than mmW face and the whole body, ii) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body, and iii) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this work, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported.