scholarly journals Preprocessing With Symmetrical Face And Gamma Correction For Face Recognition Under Varying Illumination With Robust Regression Classification

Kursor ◽  
2018 ◽  
Vol 9 (2) ◽  
Author(s):  
Eva Y Puspaningrum ◽  
Budi Nugroho ◽  
Andri Istifariyanto
Keyword(s):  
Face Recognition ◽  
Robust Regression ◽  
Facial Recognition ◽  
Face Image ◽  
Lighting Conditions ◽  
Average Accuracy ◽  
The Face ◽  
Symmetrical Object ◽  
The Right ◽  
Symmetric Property

Facial recognition is one of the most popular issues in the field of pattern recognition.Face recognition with uncontrolled lighting conditions is more significant than thephysical characteristics of individual faces. Uncontrolled lighting from the right and leftcan affect the face image. A lot of research on facial recognition, but little attention givento the face image is symmetrical object. Several studies to explore and exploit thesymmetrical properties of the face for face recognition were performed. In this paper, wepropose a pre-processing method to solve one of the common problems in facial imageswith varying illumination. We utilize the symmetric property of the face then performedgamma correction then classified using Robust Regression. The results of this experimentgot an average accuracy of 94.31% and the proposed technique improves recognitionaccuracy especially in images with extreme lighting conditions using gamma correctionparameters γ = 0.3.

scholarly journals MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss

2021 ◽  
Vol 11 (16) ◽  
pp. 7310
Author(s):  
Hongxia Deng ◽  
Zijian Feng ◽  
Guanyu Qian ◽  
Xindong Lv ◽  
Haifang Li ◽  
...  
Keyword(s):  
Face Recognition ◽  
Facial Recognition ◽  
Positional Information ◽  
Recognition Algorithm ◽  
Face Image ◽  
Convolutional Network ◽  
Public Places ◽  
Large Margin ◽  
The Face ◽  
Key Features

The world today is being hit by COVID-19. As opposed to fingerprints and ID cards, facial recognition technology can effectively prevent the spread of viruses in public places because it does not require contact with specific sensors. However, people also need to wear masks when entering public places, and masks will greatly affect the accuracy of facial recognition. Accurately performing facial recognition while people wear masks is a great challenge. In order to solve the problem of low facial recognition accuracy with mask wearers during the COVID-19 epidemic, we propose a masked-face recognition algorithm based on large margin cosine loss (MFCosface). Due to insufficient masked-face data for training, we designed a masked-face image generation algorithm based on the detection of the detection of key facial features. The face is detected and aligned through a multi-task cascaded convolutional network; and then we detect the key features of the face and select the mask template for coverage according to the positional information of the key features. Finally, we generate the corresponding masked-face image. Through analysis of the masked-face images, we found that triplet loss is not applicable to our datasets, because the results of online triplet selection contain fewer mask changes, making it difficult for the model to learn the relationship between mask occlusion and feature mapping. We use a large margin cosine loss as the loss function for training, which can map all the feature samples in a feature space with a smaller intra-class distance and a larger inter-class distance. In order to make the model pay more attention to the area that is not covered by the mask, we designed an Att-inception module that combines the Inception-Resnet module and the convolutional block attention module, which increases the weight of any unoccluded area in the feature map, thereby enlarging the unoccluded area’s contribution to the identification process. Experiments on several masked-face datasets have proved that our algorithm greatly improves the accuracy of masked-face recognition, and can accurately perform facial recognition with masked subjects.

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

Hybrid Biometrics and Watermarking Authentication

2017 ◽  
pp. 37-61
Author(s):  
Kareem Kamal A. Ghany ◽  
Hossam M. Zawbaa
Keyword(s):  
Face Recognition ◽  
Hybrid Approach ◽  
Personal Characteristics ◽  
Face Image ◽  
Discrete Wavelet ◽  
Dna Encoding ◽  
Security Authentication ◽  
The Face ◽  
Tools And Techniques

There are many tools and techniques that can support management in the information security field. In order to deal with any kind of security, authentication plays an important role. In biometrics, a human being needs to be identified based on some unique personal characteristics and parameters. In this book chapter, the researchers will present an automatic Face Recognition and Authentication Methodology (FRAM). The most significant contribution of this work is using three face recognition methods; the Eigenface, the Fisherface, and color histogram quantization. Finally, the researchers proposed a hybrid approach which is based on a DNA encoding process and embedding the resulting data into a face image using the discrete wavelet transform. In the reverse process, the researchers performed DNA decoding based on the data extracted from the face image.

scholarly journals Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 307 ◽  
Author(s):  
Ngo Tung Son ◽  
Bui Ngoc Anh ◽  
Tran Quy Ban ◽  
Le Phuong Chi ◽  
Bui Dinh Chien ◽  
...  
Keyword(s):  
Face Recognition ◽  
Motion Blur ◽  
Security Monitoring ◽  
Flexible System ◽  
Lighting Conditions ◽  
Satisfactory Performance ◽  
Measurement Results ◽  
The Face ◽  
Recognition Systems

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

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.

scholarly journals 3D Face Image Inpainting with Generative Adversarial Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tongxin Wei ◽  
Qingbao Li ◽  
Jinjin Liu ◽  
Ping Zhang ◽  
Zhifeng Chen
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Image Inpainting ◽  
Face Image ◽  
Edge Information ◽  
3D Face ◽  
Face Images ◽  
The Face ◽  
Match Effect ◽  
Traditional Image

In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.

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.

Face recognition impairments

1992 ◽  
Vol 335 (1273) ◽  
pp. 47-54 ◽  
Keyword(s):  
Face Recognition ◽  
Processing System ◽  
Right Cerebral Hemisphere ◽  
New Findings ◽  
Functional Models ◽  
The Face ◽  
Types Of Information ◽  
The Right ◽  
Types Of Error ◽  
Schematic Form

Face recognition impairments are often found in the context of brain injury involving the right cerebral hemisphere. Recognition impairments can be dissociated from impairments affecting the processing of other types of information carried by the face, such as expression. The face recognition impairments themselves take different forms, corresponding to idealized stages or levels of recognition. These types of error can also arise as transitory phenomena in normal everyday life. From these observations, psychologists have proposed functional models that characterize the organization of the face processing system in schematic form. Such models provide useful ways of summarizing what is known. More importantly, they also allow new findings to act as tests of each model’s usefulness by the extent to which they can be readily accommodated or force revision. Examples of this are briefly considered, including delusional misidentification, impaired learning of new faces, disordered attention to faces, ‘covert’ recognition in prosopagnosia, and unawareness of impaired face recognition.

Human Face Recognition and the Face Image Set′s Topology

1994 ◽  
Vol 59 (2) ◽  
pp. 254-261 ◽  
Author(s):  
M. Bichsel ◽  
A.P. Pentland
Keyword(s):  
Face Recognition ◽  
Face Image ◽  
Human Face ◽  
Human Face Recognition ◽  
The Face ◽  
Image Set
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