Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique: A Review

2018 ◽  
Vol 8 (6) ◽  
pp. 51
Author(s):  
Diksha Anand ◽  
Kamal Gupta
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Replay Attack ◽  
Detection Systems ◽  
System A ◽  
The Face ◽  
Alternative Means ◽  
Authentication System ◽  
Real Person ◽  
Face Spoofing

Face recognition is an alternative means to authenticate a person in different applications for access control. Instead of many improvements, this method is prone to various attacks like photos, 3D masks and video replay attack. Due to these attacks, system should require a face spoof detection system. A face spoof detection systems have an ability to identify whether a face is from a real person or a fake image. Face spoofing effect the image by adding deformation in it and also degrades the image pattern quality. Face spoofing detection system automatically identifies the human face is a true face or a fake face. In today's era, face recognition method is widely used to authenticate the face (like for unlocking mobile phones etc.) and providing access to the services or facilities but some intruders use various trick to crack the authentication system by presenting the false face in front of the authentication system, so it become necessity to prevent our face authentication system from face spoofing attack. So the choice of the technique to detect the face spoofing attack should be accurate and highly efficient.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 409-416
Author(s):  
Apurva Yawalikar ◽  
Prof. U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

scholarly journals DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Cheng ◽  
Hongfei Wang ◽  
Jingmei Zhou ◽  
Hui Chang ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Feature Fusion ◽  
Texture Features ◽  
Replay Attack ◽  
Attention Network ◽  
Detection Failure ◽  
The Face ◽  
Recognition Systems ◽  
Common Face

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

scholarly journals Viewpoint Robustness of Automated Facial Action Unit Detection Systems

2021 ◽  
Vol 11 (23) ◽  
pp. 11171
Author(s):  
Shushi Namba ◽  
Wataru Sato ◽  
Sakiko Yoshikawa
Keyword(s):  
Prediction Accuracy ◽  
Detection System ◽  
Action Unit ◽  
Action Detection ◽  
Facial Action ◽  
Detection Systems ◽  
The Face ◽  
Overall Performance ◽  
Action Unit Detection ◽  
Facial Images

Automatic facial action detection is important, but no previous studies have evaluated pre-trained models on the accuracy of facial action detection as the angle of the face changes from frontal to profile. Using static facial images obtained at various angles (0°, 15°, 30°, and 45°), we investigated the performance of three automated facial action detection systems (FaceReader, OpenFace, and Py-feat). The overall performance was best for OpenFace, followed by FaceReader and Py-Feat. The performance of FaceReader significantly decreased at 45° compared to that at other angles, while the performance of Py-Feat did not differ among the four angles. The performance of OpenFace decreased as the target face turned sideways. Prediction accuracy and robustness to angle changes varied with the target facial components and action detection system.

scholarly journals Measurement of Face Detection Accuracy Using Intensity Normalization Method and Homomorphic Filtering

2017 ◽  
Vol 2 (1) ◽  
pp. 107
Author(s):  
I Nyoman Gede Arya Astawa ◽  
I Ketut Gede Darma Putra ◽  
I Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Recognition System ◽  
Face Image ◽  
Normalization Method ◽  
Detection Accuracy ◽  
Normalization Methods ◽  
Homomorphic Filtering ◽  
The Face ◽  
Intensity Normalization

One of the factors that affects the detection system or face recognition is lighting. Image color processing can help the face recognition system in poor lighting conditions. In this study, homomorphic filtering and intensity normalization methods used to help improve the accuracy of face image detection. The experimental results show that the non-uniform of the illumination of the face image can be uniformed using the intensity normalization method with the average value of Peak Signal to Noise Ratio (PSNR) obtained from the whole experiment is 22.05314 and the average Absolute Mean Brightness Error (AMBE) value obtained is 6.147787. The results showed that homomorphic filtering and intensity normalization methods can be used to improve the detection accuracy of a face image.

Oil and Gas Offshore Pipeline Leak Detection System: A Feasibility Study

2014 ◽  
Vol 699 ◽  
pp. 891-896 ◽  
Author(s):  
Mohamad Fani Sulaima ◽  
F. Abdullah ◽  
Wan Mohd Bukhari ◽  
Fara Ashikin Ali ◽  
M.N.M. Nasir ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Oil And Gas ◽  
Detection System ◽  
Leak Detection ◽  
Inspection Time ◽  
Related Data ◽  
Detection Systems ◽  
System A ◽  
Detection Technology ◽  
Offshore Pipeline

Pipelines leaks normally begin at poor joints, corrosions and cracks, and slowly progress to a major leakage. Accidents, terror, sabotage, or theft are some of human factor of pipeline leak. The primary purpose of Pipeline leak detection systems (PLDS) is to assist pipeline operators in detecting and locating leaks earlier. PLDS systems provide an alarm and display other related data to the pipeline operators for their decision-making. It is also beneficial because PLDS can enhance their productivity by reduced downtime and inspection time. PLDS can be divided into internally based or computational modeling PLDS Systems and external hardware based PLDS. The purpose of this paper is to study the various types of leak detection systems based on internally systemtodefine a set of key criteria for evaluating the characteristics of this system and provide an evaluation method of leak detection technology as a guideline of choosing the appropriate system.

scholarly journals Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 3309-3314
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Attack Detection ◽  
Detection Methods ◽  
Deep Convolutional Neural Networks ◽  
Replay Attack ◽  
Multiple Features ◽  
Spoofing Detection ◽  
Occurrence Matrix ◽  
Face Spoofing

Nowadays, face biometric-based access control systems are becoming ubiquitous in daily life while they are still vulnerable to spoofing attacks. Developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning-based detection methods cannot be updated to optimum due to limited data. In this paper,a highly accurate face spoof detection system using multiple features and deep learning is proposed. The input video is broken into frames using content-based frame extraction. From each frame, the face of the person is cropped.From the cropped images multiple features like Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN). Training and testing are performed separately by using collected sample data.Experiments on the standard spoof database called Replay-Attack database the proposed system outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

scholarly journals Face Recognition Using Livenessnet

2021 ◽  
pp. 143-148
Author(s):  
Shajahan K ◽  
Rathish Rai D ◽  
Ravishankara
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Access Control ◽  
Smart Card ◽  
Deep Neural Networks ◽  
Digital Technologies ◽  
Recognition Method ◽  
Public Places ◽  
The Face ◽  
Face Spoofing

Every person's face is unique, although have the same structure such as noise, eyes, lips, etc. but it can vary strikingly. It’s within this variance which lies in the distinguishing characteristics that can be used to identify one person from another. Face recognition is a popular concept which is commonly used in surveillance cameras at public places for security purposes. With the advancement of digital technologies, the demand for security to provide access control is increasing. It uses various methods of authentication to keep all details secure, such as a system focused on encrypted user name & password, smart card, biometrics, etc. The “Face Recognition using DNN with LivenessNet” presents a face recognition method based on deep neural networks for liveness. Any algorithm is considered to be efficient only if it is robust and accurate. It provides accurate results with face spoofing quickly and efficiently. The main advantage of using this technique is identifying the uniqueness in the datasets by capturing the real-time face data through different modes & jitter. It provides accurate face recognition model which can be used for safety and security purpose.

scholarly journals A Review on Face Anti-Spoofing

2021 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Rizky Naufal Perdana ◽  
Igi Ardiyanto ◽  
Hanung Adi Nugroho
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Real World ◽  
Biometric System ◽  
Security Issue ◽  
Security Technology ◽  
The Face ◽  
Paper Review ◽  
Recognition Systems ◽  
Face Spoofing

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

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