Millimetre wave person recognition: Hand-crafted vs learned features

Ester Gonzalez-Sosa, Ruben Vera-Rodriguez, Julian Fierrez, Vishal M. Patel
2017 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)  
Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract Imaging using millimeter waves (mmWs) has many advantages including ability to penetrate obscurants such as clothes and polymers. Although conceal weapon detection has been the predominant mmW imaging application, in this
more » ... er, we aim to gain some insight about the potential of using mmW images for person recognition. 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 CNN models. Results suggest that: i) mmW torso region is more discriminative than mmW face and the entire 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.
doi:10.1109/isba.2017.7947692 dblp:conf/isba/Gonzalez-SosaVF17 fatcat:y4vxjnfpprar3b5tuwirsxcd54