Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry

Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not.

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Face Detection Model for Thermal Images

10.1109/icast51016.2020.9557718 ◽

2020 ◽

Author(s):

Hendrick ◽

Surfa Yondri ◽

Rahmat Hidayat ◽

Albar Albar ◽

Hanifa Fitri ◽

...

Keyword(s):

Face Detection ◽

Thermal Images ◽

Detection Model

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Real Time Face Detection System Based Edge Restoration and Nested K-Means at Frontal View

Fuzzy Systems and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/11881599_148 ◽

2006 ◽

pp. 1192-1200

Author(s):

Hyun Jea Joo ◽

Bong Won Jang ◽

Md. Rezaul Bashar ◽

Phill Kyu Rhee

Keyword(s):

Real Time ◽

Face Detection ◽

Detection System ◽

Frontal View

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Inner-Canthus Localization of Thermal Images in Face-View Invariant

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.8.6.3903 ◽

2018 ◽

Vol 8 (6) ◽

pp. 2570

Author(s):

Hurriyatul Fitriyah ◽

Edita Rosana Widasari ◽

Rekyan Regasari Mardi Putri

Keyword(s):

Thermal Images ◽

Face View

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Face with Mask Detection in Thermal Images Using Deep Neural Networks

Sensors ◽

10.3390/s21196387 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6387

Author(s):

Natalia Głowacka ◽

Jacek Rumiński

Keyword(s):

Public Health ◽

Face Detection ◽

Thermal Imaging ◽

Deep Neural Networks ◽

Average Precision ◽

Thermal Images ◽

Detection Algorithms ◽

Health Control ◽

The Face ◽

The One

As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus protective face masks. As part of this work, a set of thermal images was prepared containing over 7900 images of faces with and without masks. Selected raw data preprocessing methods were also investigated to analyze their influence on the face detection results. It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. The best model turned out to be the one based on Yolov3 model (mean average precision—mAP, was at least 99.3%, while the precision was at least 66.1%). Inference time of the models selected for evaluation on a small and cheap platform allows them to be used for many applications, especially in apps that promote public health.

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Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar

Journal Of Computer Networks, Architecture and High Performance Computing ◽

10.47709/cnahpc.v3i2.1058 ◽

2021 ◽

Vol 3 (2) ◽

pp. 213-222

Author(s):

Robin Robin ◽

Aldrick Handinata ◽

Wenripin Chandra

Keyword(s):

Face Recognition ◽

Face Detection ◽

Facial Recognition ◽

Prototype System ◽

Body Parts ◽

Test Results ◽

Haar Feature ◽

System Prototype

Facial recognition is one of the most popular way to authenticate user into a system. This method is preferable considering the tendency of users for using the same password across multiple sites which made the user has already made his own account securities in vulnerable states. Using biometrics might supply solutions to solve this problem and facial recognition is one of the best biometric methods can be apply as a digital account security solution. This study to design a prototype system implementing facial recognition to verify users to measure how accurate these methods are. The method used here is Viola-Jones for face detection, Eigenface and Haar feature for face recognition from the OpenCV. The system was designed in Java. Based on the test results from the system designed, system can recognize user face with 100% accuracy if faces are shot in a well desirable condition. The system is able to recognize the user's face with various expressions including with or without glasses. However, the system has difficulty in recognizing user’s face in facing up, down, sideways position or blocked by accessories or body parts such as hands. After some experiment, it was proven that the system designed is accurate, reliable and safe enough to be implemented to digital authorization process.

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