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.

scholarly journals Thermal Image Generation for Robust Face Recognition

2022 ◽  
Vol 12 (1) ◽  
pp. 497
Author(s):  
Vicente Pavez ◽  
Gabriel Hermosilla ◽  
Francisco Pizarro ◽  
Sebastián Fingerhuth ◽  
Daniel Yunge
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Generator System ◽  
Visible Image ◽  
Thermal Images ◽  
Face Recognition System ◽  
Latent Space ◽  
Visible Images ◽  
Robust Face Recognition ◽  
Thermal Face

This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.

An efficient object detection using OFSA for thermal imaging

2020 ◽  
pp. 002072092094443
Author(s):  
V Teju ◽  
D Bhavana
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Thermal Imaging ◽  
Visual Recognition ◽  
Transfer Functions ◽  
Recognition System ◽  
Visual Images ◽  
Thermal Images ◽  
Security Issues ◽  
The Matrix

The demand for identifying a reliable person is increased because of security issues in our daily life. At present, to identify a person biometric technique such as face recognition is introduced. Since,a person with abnormal behaviour recognition system has reached certain level, their accomplishments in real time applications are restricted by challenges, such as illumination variations. The present visual recognition system is good at controlled illumination conditions and thermal face recognition system is better for detecting disguised persons or when there is no illumination control. Hence, a hybrid system which uses both visual and thermal images for recognising a person is better. The objective of this research is to implement a method which improves the quality of the image by fusing visual and thermal imaging images. Our research methodology has introduced to enhance servo line camera images. Nonlinear image transfer functions were introduced,and the parameters associated with those functions are determined by image statistics for making adaptive algorithms. Next methodswereintroduced for registering the visual images to their consequent thermal images. To get a transformation matrix for the registration, the landmarks in the images are first detected and a subset of those landmarks were selected to obtain the matrix, we propose a hybrid algorithm for detection, tracking and classification using OFSA algorithm to fuse the registered thermal and visual images. In this research, we focus on object detection using OFSA algorithm for more accuracy.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

scholarly journals A FACIAL RECOGNITION USING OPEN COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.C K Gomathy ◽  
T. suneel ◽  
Y.Jeeevan Kumar Reddy
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Functional Space ◽  
Recognition System ◽  
Statistical Technique ◽  
Exciting Field ◽  
Two Dimensional Image ◽  
Face Recognition System ◽  
The Face ◽  
Amount Of Knowledge

The Face recognition and image or video recognition are popular research topics in biometric technology. Real-time face recognition is an exciting field and a rapidly evolving issue. Key component analysis (PCA) may be a statistical technique collectively called correlational analysis . The goal of PCA is to scale back the massive amount of knowledge storage to the dimensions of the functional space required to render the face recognition system. The wide one-dimensional pixel vector generated from the two-dimensional image of the face and therefore the basic elements of the spatial function are designed for face recognition using PCA. this is often the projection of your own space. Sufficient space is decided by the brand. specialise in the eigenvectors of the covariance matrix of the fingerprint image collection. i'm building a camera-based real-time face recognition system and installing an algorithm. Use OpenCV, Haar Cascade, Eigen face, Fisher Face, LBPH and Python for program development.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

A TIED-MIXTURE 2D HMM FACIAL IMAGE RETRIEVAL SYSTEM

2004 ◽  
Vol 13 (05) ◽  
pp. 1133-1146
Author(s):  
H. OTHMAN ◽  
T. ABOULNASR
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Training Data ◽  
Expected Improvement ◽  
Model Case ◽  
Face Recognition System ◽  
Image Retrieval System ◽  
The Face ◽  
Institute Of Technology ◽  
Observation Space

In this paper, the effect of mixture tying on a second-order 2D Hidden Markov Model (HMM) is studied as applied to the face recognition problem. While tying HMM parameters is a well-known solution in the case of insufficient training data that leads to nonrobust estimation, it is used here to improve the overall performance in the small model case where the resolution in the observation space is the main problem. The fully-tied-mixture 2D HMM-based face recognition system is applied to the facial database of AT&T and the facial database of Georgia Institute of Technology. The performance of the proposed 2D HMM tied-mixture system is studied and the expected improvement is confirmed.

scholarly journals A Web-Based Mobile Attendance System with Facial Recognition Feature

2020 ◽  
Vol 14 (05) ◽  
pp. 193
Author(s):  
Noradila Nordin ◽  
Nurul Husna Mohd Fauzi
Keyword(s):  
Face Recognition ◽  
Mobile Devices ◽  
Conventional Method ◽  
Facial Recognition ◽  
Recognition System ◽  
Web Based ◽  
Face Recognition System ◽  
The Face ◽  
System Prototype

Attendance marking in a classroom is one of the methods used to track the student’s presence in the lecture. The conventional method that is being enforced has shown to be vulnerable, inaccurate and time-consuming especially in a large classroom. It is difficult to identify absentees and proxy attendees based on the conventional attendance marking method. In order to overcome the challenges faced in the conventional method, a web-based mobile attendance system with facial recognition feature is proposed. It incorporated the existing mobile devices with a camera and the face recognition system to allow the attendance system to be used in classrooms automatically and efficiently with minor implementation requirements. The system prototype received positive responses from the volunteers who tested the system to replace the conventional attendance marking.

The Safety Research for Face Recognition System Based on Image Feature and Digital Watermarking

2012 ◽  
Vol 224 ◽  
pp. 485-488
Author(s):  
Fei Li ◽  
Yuan Yuan Wang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Image ◽  
Image Feature ◽  
Safety Research ◽  
The Face ◽  
Information Image ◽  
Image Integrity ◽  
Recognition Software

Abstract: In order to solve the easily copied problem of images in face recognition software, an algorithm combining the image feature with digital watermark is presented in this paper. As watermark information, image feature of the adjacent blocks are embedded to the face image. And primitive face images are not needed when recovering the watermark. So face image integrity can be well confirmed, and the algorithm can detect whether the face image is the original one and identify whether the face image is attacked by malicious aim-such as tampering, replacing or illegally adding. Experimental results show that the algorithm with good invisibility and excellent robustness has no interference on face recognition rate, and it can position the specific tampered location of human face image.

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