Facial Recognition for Drunk People Using Thermal Imaging

Agustin Sancen-Plaza,Carlos Villaseñor-Mora,José A Padilla-Medina,Luis M Contreras-Medina,Juan J Martínez-Nolasco,Alejandro Israel Barranco-Gutiérrez

doi:10.1155/2020/1024173

Abstract

Face recognition using thermal imaging has the main advantage of being less affected by lighting conditions compared to images in the visible spectrum. However, there are factors such as the process of human thermoregulation that cause variations in the surface temperature of the face. These variations cause recognition systems to lose effectiveness. In particular, alcohol intake causes changes in the surface temperature of the face. It is of high relevance to identify not only if a person is drunk but also their identity. In this paper, we present a technique for face recognition based on thermal face images of drunk people. For the experiments, the Pontificia Universidad Católica de Valparaíso-Drunk Thermal Face database (PUCV-DTF) was used. The recognition system was carried out by using local binary patterns (LBPs). The LBP features were obtained from the bioheat model from thermal image representation and a fusion of thermal images and a vascular network extracted from the same image. The feature vector for each image is formed by the concatenation of the LBP histogram of the thermogram with an anisotropic filter and the fused image, respectively. The proposed technique has an average percentage of 99.63% in the Rank-10 cumulative classification; this performance is superior compared to using LBP in thermal images that do not use the bioheat model.

Highlights

Ese advantages have motivated researchers to propose different models that represent determining information for the identification of individuals
Blood perfusion models were used in order to mitigate the effects caused by the increase in temperature [7] caused by alcohol intake. e perfusion model was performed as an image preprocessing. is feature is ideal for scenarios where conditions that cause changes in the surface temperature of the face can occur
Recognition systems that use infrared face images are classified by Arya et al [23] as (1) based on face recognition techniques in images with classical methods; (2) based on feature extraction, and (3) based on the multimodal analysis. e system proposed in this study is based on feature extraction and was designed in three stages denoted as (1) generation of the bioheat transfer model; (2) fusion of thermograms with vascular networks; and (3) feature extraction

Summary

Introduction

Ese advantages have motivated researchers to propose different models that represent determining information for the identification of individuals. Ese temperature changes cause the heat dispersion zones of the skin to activate and these, in turn, can be obtained in the form of an image by a thermal camera. E first heat dispersion model using LWIR face images was proposed by Wu et al [7].

Results

Conclusion