scholarly journals Evaluation of Face Detection Algorithms for the Bank Client Identity Verification

2017 ◽  
Vol 42 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Maciej Szczodrak ◽  
Andrzej Czyżewski
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
User Interaction ◽  
Identity Verification ◽  
Detection Algorithms ◽  
Video Recordings ◽  
Verification System ◽  
The Face ◽  
Usb Camera ◽  
Made In

Abstract Results of investigation of face detection algorithms efficiency in the banking client visual verification system are presented. The video recordings were made in real conditions met in three bank operating outlets employing a miniature industrial USB camera. The aim of the experiments was to check the practical usability of the face detection method in the biometric bank client verification system. The main assumption was to provide a simplified as much as possible user interaction with the application. Applied algorithms for face detection are described and achieved results of face detection in the real bank environment conditions are presented. Practical limitations of the application based on encountered problems are discussed.

scholarly journals Face with Mask Detection in Thermal Images Using Deep Neural Networks

Sensors ◽  
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.

scholarly journals A Joint Learning Approach to Face Detection in Wavelet Compressed Domain

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Szu-Hao Huang ◽  
Shang-Hong Lai
Keyword(s):  
Face Detection ◽  
Learning Algorithm ◽  
Feature Space ◽  
Detection Algorithm ◽  
Feature Representation ◽  
Compressed Domain ◽  
Discrimination Power ◽  
Detection Algorithms ◽  
The Face ◽  
Active Research

Face detection has been an important and active research topic in computer vision and image processing. In recent years, learning-based face detection algorithms have prevailed with successful applications. In this paper, we propose a new face detection algorithm that works directly in wavelet compressed domain. In order to simplify the processes of image decompression and feature extraction, we modify the AdaBoost learning algorithm to select a set of complimentary joint-coefficient classifiers and integrate them to achieve optimal face detection. Since the face detection on the wavelet compression domain is restricted by the limited discrimination power of the designated feature space, the proposed learning mechanism is developed to achieve the best discrimination from the restricted feature space. The major contributions in the proposed AdaBoost face detection learning algorithm contain the feature space warping, joint feature representation, ID3-like plane quantization, and weak probabilistic classifier, which dramatically increase the discrimination power of the face classifier. Experimental results on the CBCL benchmark and the MIT + CMU real image dataset show that the proposed algorithm can detect faces in the wavelet compressed domain accurately and efficiently.

Human Face Detection Method Based on Skin Color Model

2013 ◽  
Vol 706-708 ◽  
pp. 1877-1881
Author(s):  
San Tang
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Color Space ◽  
Color Model ◽  
Knowledge Based ◽  
Face Area ◽  
The Face ◽  
Skin Color Model ◽  
Active Research

Face detection is the first step of face recognition, and is a very active research topic in the filed of computer vision and pattern recognition. A skin color model based face detection method for chromatic images is proposed in this paper. The H-CgCr skin color model is established by taking advantage of the color pixels clustering distribution in the HSV and YCgCr color space. The noises are eliminated based on skin color segmentation, and the face candidate region is judged according to knowledge-based, finally, the position of the face area is marked by the box. The experimental results demonstrate that the proposed method is feasible and effective.

scholarly journals A New Hybrid Approach on Face Detection and Recognition

2020 ◽  
Author(s):  
Saloni Dwivedi ◽  
Nitika Gupta
Keyword(s):  
Face Detection ◽  
Research Work ◽  
Hybrid Approach ◽  
Initial Step ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
The Face ◽  
Face Detection And Recognition ◽  
Passport Control ◽  
Detection And Recognition

Face detection and recognition is an important paradigm when we consider the biometric based systems. Amongvarious biometric elements, the face is the most reliable one and can be easily observed even from a distance as compared to iris or fingerprint which needs to be closely observed to use them for any kind of detection and recognition. Challenges faced by face detection algorithms often involve the presence of facial features such as beards, moustaches, and glasses, facial expressions,and occlusion of faces like surprised or crying. Another problem is illumination and poor lighting conditions such as in video surveillance cameras image quality and size of the image as in passport control or visa control. Complex backgrounds also make it extremely hard to detect faces. In this research work, a number of methods and research paradigms pertaining to face detection and recognition is studied at length and evaluate various face detection and recognition methods, provide a complete solutionfor image-based face detection and recognition with higher accuracy, a better response rate as an initial step for videosurveillance.

Research of Face Detection Method Based on Skin Color Feature

2013 ◽  
Vol 373-375 ◽  
pp. 478-482
Author(s):  
Qing Ye
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Recognition System ◽  
Human Face ◽  
Color Feature ◽  
Binary Images ◽  
Face Recognition System ◽  
The Face ◽  
To Receive

Human face detection is the first critical step of face recognition system. This paper proposed a face detection method based on skin color feature. Firstly, the method of building a skin color feature from RGB to YCbCr and extracting skin color region according the chrominance similarity was used to extract the face gray image. Secondly, image smoothness and image binarization were used to receive the binary image, then mathematical morphology operators were used to eliminate the binary images noise and disturbance. At last, human face regions are detected through projection operation. The result of experimentation affirms that the method is efficient to detect human face.

scholarly journals An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

2022 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
Dweepna Garg ◽  
Priyanka Jain ◽  
Ketan Kotecha ◽  
Parth Goel ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Machine Learning Techniques ◽  
Face Shape ◽  
Detection Algorithms ◽  
Accuracy And Precision ◽  
Learning Techniques ◽  
Proposed Model ◽  
The Face ◽  
Scale Anchor

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

scholarly journals Facial Recognition Approach using ABCD Algorithm for Cancer Treatment

2019 ◽  
Vol 8 (9) ◽  
pp. 3408-3411
Keyword(s):  
Face Detection ◽  
Specific Area ◽  
Skin Lesions ◽  
Input Image ◽  
Test Case ◽  
Data Sets ◽  
Abnormal Growth ◽  
Detection Algorithms ◽  
Drastic Increase ◽  
The Face

Malignant melanoma is one of the generally known cancers due to the changes in the skin behaviour that cause a drastic increase in numerous melanomas which is seen among many white-skinned people. To detect and classify skin lesions, we require a fast and reliable system. The face detection algorithms are used in which, an image dataset is formed and from that several images are tested for the presence of a face. When the face is present, the image is selected for further processing and separate features are detected. The presence of the face, along with two eyes, nose, mouth and lips are necessary for the face detection to work efficiently. A specific area of the face is selected as a test case and the skin irregularity is checked for abnormal features are present or not. An algorithm by the name Asymmetry, Border, Color and Dermatoscopic features (ABCD) is developed which will check the skin parameters and help figure out the presence of abnormal growth. The accuracy of detection will depend upon the clarity of the input image, the brightness and the sharpness. The later part of the project will stress the importance of data exports from the working data sets to a portable format

A study of interface sliding at F.C.C.-B.C.C. boundaries in a Cu-Zn alloy

1978 ◽  
Vol 36 (1) ◽  
pp. 422-423
Author(s):  
F. Monchoux ◽  
A. Rocher ◽  
J.L. Martin
Keyword(s):  
Face Centered Cubic ◽  
Creep Tests ◽  
High Voltage Electron Microscopy ◽  
Diffusion Treatment ◽  
Voltage Electron ◽  
The Face ◽  
Face Centered ◽  
Interface Sliding ◽  
Zn Alloy ◽  
Made In

Interphase sliding is an important phenomenon of high temperature plasticity. In order to study the microstructural changes associated with it, as well as its influence on the strain rate dependence on stress and temperature, plane boundaries were obtained by welding together two polycrystals of Cu-Zn alloys having the face centered cubic and body centered cubic structures respectively following the procedure described in (1). These specimens were then deformed in shear along the interface on a creep machine (2) at the same temperature as that of the diffusion treatment so as to avoid any precipitation. The present paper reports observations by conventional and high voltage electron microscopy of the microstructure of both phases, in the vicinity of the phase boundary, after different creep tests corresponding to various deformation conditions.Foils were cut by spark machining out of the bulk samples, 0.2 mm thick. They were then electropolished down to 0.1 mm, after which a hole with thin edges was made in an area including the boundary

Pengoptimalan Air Leri dalam Pembuatan Sabun Pembersih Wajah Alami yang Ekonomis

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Rahmawati Rahmawati ◽  
Trimayasari Trimayasari ◽  
Ghozali Akhmad Mustaqim ◽  
Wening Dwi Prastiwi ◽  
Emas Agus Prastyo Wibowo
Keyword(s):  
Amino Acids ◽  
Fine Particles ◽  
Hard Water ◽  
Essential Amino Acids ◽  
Ultraviolet Rays ◽  
Starch Granules ◽  
Facial Skin ◽  
Skin Cells ◽  
The Face ◽  
Made In

AbstractSoap facial cleanser is needed to keep the facial skin to keep them clean and healthy. The purpose of this study to make soap cleanser with natural materials such as hard water deposits leri. This is because the use of leri water starch or starch granules of fine particles contained in water leri dansel dust can shed the dead skin on the face because of the essential amino acids contained can regenerate skin cells. In addition, water leri can brighten the face because the leri water oryzanol contain substances that can update the development and formation of the pigment melanin, which is effectively to ward off ultraviolet rays. The process of making soap using the principle of saponification reaction, namely the reaction between the oil and the KOH/NaOH. Facial cleansing soap made in this study is solid soap. Based on the results of quality test, soap solid leri water has a pH of 11.1, saponification number is 33, the water content of 46% as well as respondents to the test aspects of aroma and foam shows good results so this water leri treatment can be an alternative solution to prevent the use of soap facial cleansers that contain harmful chemicals. Keywords: air leri, soap cleanser, saponification  AbstrakSabun pembersih wajah sangat diperlukan untuk menjaga kulit wajah agar tetap bersih dan sehat. Tujuan dari penelitian ini untuk membuat sabun pembersih wajah dengan bahan alami berupa endapan air leri. Penggunaan air leri ini dikarenakan butiran partikel starch atau pati halus yang terdapat dalam air leri dapat merontokkan debu dansel kulit mati pada wajah karena asam amino esensial yang terkandung dapat meregenerasi sel-sel kulit. Selain itu, air leri dapat mencerahkan wajah karena air leri mengandung zat oryzanol yang dapat memperbarui perkembangan dan pembentukan pigmen melanin, yang efektif guna menangkal sinar ultraviolet. Proses pembuatan sabun menggunakan prinsip reaksi saponifikasi, yaitu reaksi antara minyak dan KOH/NaOH. Sabun pembersih wajah yang dibuat dalam penelitian ini ialah sabun padat. Berdasarkan hasil uji mutu, sabun air leri padat memiliki pH 11,1, angka penyabunan sebesar 33 kadar air 46 kadar air 46 % serta uji responden terhadap aspek aroma dan busa yang menunjukkan hasil cukup baik sehingga pengolahan air leri ini dapat menjadi solusi alternative untuk mencegah penggunaan sabun pembersih wajah yang mengandung bahan kimia berbahaya. Kata kunci: air leri, sabun pembersih wajah, saponifikasi 

Real AdaBoost face detection method based on multi-threshold

2009 ◽  
Vol 29 (8) ◽  
pp. 2098-2100
Author(s):  
Shi-ming SUN ◽  
Qing PAN ◽  
You-fang JI
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Real Adaboost
Sign in / Sign up

Export Citation Format

Share Document