scholarly journals Thermal Face Recognition Under Temporal Variation Conditions

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 9663-9672 ◽  
Author(s):  
Gabriel Hermosilla Vigneau ◽  
Jose Luis Verdugo ◽  
Gonzalo Farias Castro ◽  
Francisco Pizarro ◽  
Esteban Vera
Keyword(s):  
Face Recognition ◽  
Temporal Variation ◽  
Thermal Face

Accuracy enhanced thermal face recognition

2013 ◽  
Vol 61 ◽  
pp. 200-207 ◽  
Author(s):  
Chun-Fu Lin ◽  
Sheng-Fuu Lin
Keyword(s):  
Face Recognition ◽  
Thermal Face

An Enhanced Fractal Dimension Based Feature Extraction for Thermal Face Recognition

2018 ◽  
pp. 217-226 ◽  
Author(s):  
Sandip Joardar ◽  
Arnab Sanyal ◽  
Dwaipayan Sen ◽  
Diparnab Sen ◽  
Amitava Chatterjee
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Face Recognition ◽  
Thermal Face

Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles

2015 ◽  
Author(s):  
Tarek Gaber ◽  
Alaa Tharwat ◽  
Abdelhameed Ibrahim ◽  
Vaclav Snael ◽  
Aboul Ella Hassanien
Keyword(s):  
Face Recognition ◽  
Thermal Face

scholarly journals Facial Recognition for Drunk People Using Thermal Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Agustin Sancen-Plaza ◽  
Luis M. Contreras-Medina ◽  
Alejandro Israel Barranco-Gutiérrez ◽  
Carlos Villaseñor-Mora ◽  
Juan J Martínez-Nolasco ◽  
...  
Keyword(s):  
Face Recognition ◽  
Surface Temperature ◽  
Thermal Imaging ◽  
Visible Spectrum ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Thermal Images ◽  
Average Percentage ◽  
The Face ◽  
Thermal Face

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.

An efficient deep learning model for classification of thermal face images

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Basma Abd El-Rahiem ◽  
Ahmed Sedik ◽  
Ghada M. El Banby ◽  
Hani M. Ibrahem ◽  
Mohamed Amin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Low Contrast ◽  
Content Type ◽  
Automatic Feature Extraction ◽  
Face Images ◽  
Proposed Model ◽  
Thermal Face ◽  
Deep Learning Model

PurposeThe objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.Design/methodology/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).

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