Deep learning model for deep fake face recognition and detection
Suganthi STMohamed Uvaze Ahamed AyoobkhanV. Krishna KumarNebojša BačaninK. VenkatachalamŠtěpán HubálovskýPavel Trojovský
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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.Keywords:
Deep belief network
Deep Neural Networks
Despite significant advances in Deep Face Recognition (DFR) systems, introducing new DFRs under specific constraints such as varying pose still remains a big challenge. Most particularly, due to the 3D nature of a human head, facial appearance of the same subject introduces a high intra-class variability when projected to the camera image plane. In this paper, we propose a new multi-view Deep Face Recognition (MVDFR) system to address the mentioned challenge. In this context, multiple 2D images of each subject under different views are fed into the proposed deep neural network with a unique design to re-express the facial features in a single and more compact face descriptor, which in turn, produces a more informative and abstract way for face identification using convolutional neural networks. To extend the functionality of our proposed system to multi-view facial images, the golden standard Deep-ID model is modified in our proposed model. The experimental results indicate that our proposed method yields a 99.8% accuracy, while the state-of-the-art method achieves a 97% accuracy. We also gathered the Iran University of Science and Technology (IUST) face database with 6552 images of 504 subjects to accomplish our experiments.
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Deep Neural Networks
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Scale-invariant feature transform
Three-dimensional face recognition
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Feature vector
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The vast number of researchers has been focused on pattern recognition and computer vision fields in parallel with recent technological developments over the last two decades. Some of the topics in these areas are; face detection, face recognition and gender recognition. Mostly because, the studies conducted on these areas use native ways to collect biometric data without causing any inconvenience to the subject with their contactless and free flow nature. In this paper, a new system that provides gender information using facial images is presented. The system consists of two main stages; (i) face detection and (ii) gender recognition. In the first stage, the system focuses on the detection of frontal human faces in digital images. We used a linear classifier combined with Histogram of Oriented Gradients (HOG) feature for face detection. In the second stage, two different classifiers for gender recognition were trained. The first classifier is based on Support Vector Machines (SVM) and the second is based on Convolutional Neural Networks (CNN) which is also known as Deep Learning. We used Local Binary Pattern (LBP) and HOG as features for SVM classifier, and Radial Basis Function (RBP) as its kernel. For the CNN classifier, we used GoogleNet deep neural network architecture and the optimization was performed depending on the parameters. For training of both classifiers, Labeled Faces in the Wild (LFW), IMDB and WIKI data sets were used. In our experiments, we observed that the CNN based classifier surpasses the SVM based one in terms of accuracy.
Local Binary Patterns
Three-dimensional face recognition
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Robot with face detection and recognition can be useful. Some researchers have already been tried to implement the face detection and identification to robot applications, however, these applications need a high requirement of the camera(s). This paper attempts to overcome this problem, using a webcam placed in a head robot. Haar Cascade as face detection algorithm, whereas face recognition uses a local binary pattern histogram method is used. This system enables to differentiate the face of the human with others objects and capable of identifying the person. Moreover, this robot able to follow the movement of the face.
Local Binary Patterns
Haar-like features
Object-class detection
Three-dimensional face recognition
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In order to extract the local and global facial features for effective face recognition in complex environment,a face recognition method based on histograms of oriented gradients(HOG)pyramid model was.This method constructs the HOG pyramid of face image by multiscale analysis and HOG feature maps extraction.The feature maps in each layer of HOG pyramid are respectively separated into several non-overlapping blocks from which the HOG histograms are built and concatenated into an improved feature vector that can be used as the face descriptor later.Face recognition is performed throughout the similarity calculation among above feature vectors by using nearest neighbor classifier.Face recognition experimental results on the face recognition technology(FERET)database that captured under complicate changes of light and time environments demonstrate that the proposed method is of highly discriminable ability and good robustness in face recognition.
Three-dimensional face recognition
Pyramid (geometry)
Robustness
Feature (linguistics)
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We describe a new method for recognizing humans by their face, which is robust to the variations of facial imaging conditions, with high accuracy. The human face recognition system consists of three components: i) a new face descriptor based on edge component histogram and its variance between pixels, ii) Gab or-edge components histogram for facial image representation, combining the Gab or wavelet and the proposed edge components histogram, iii) a sparse representation classifier for the face recognition. The effective and robust face recognition with high accuracy is achieved by the Gab or-edge components histogram and the sparse representation classifier. In experiments, higher face recognition performances, which are 99.45% on ETRI database and 99.41% on XM2VTS database, have been achieved.
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In order to improve the recognition rate of face,a new face recognition method is proposed based on histogram oriented gradient and discriminative multi-manifold.Firstly,the face image is divided into several patches and the patches are processed by histogram oriented gradient to form an image set.Secondly,discriminative multi-manifold is used to select the features,and least squares support vector machine is used to establish the classifier to recognized the face.Finally,the simulation experiments are carried out to test the performance on Yale and AR face data set.The experimental results show that the proposed method can improve the recognition rate and recognition speed compared with traditional face recognition methods,and has well robust for face recognition in illumination and pose conditions.
Discriminative model
Three-dimensional face recognition
Manifold (fluid mechanics)
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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.
Deep belief network
Deep Neural Networks
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Glasses detection is one of attractive tasks in image processing since it increases the performance of face recognition systems. In this study, we aimed to detect the glasses on face images automatically. In order to do this, we trained a classifier with Labelled Faces in the Wild Home (LFW) dataset to decide whether a person wear glasses or not on face images. Before classification process, image patches are extracted from aligned face images and a preprocessing was performed on them. After preprocessing step, feature vectors are formed with Histogram of Oriented Gradients (HOG) method from image patches. Due to high dimensionality of the feature vectors, dimensionality reduction was done using Principal Component Analysis (PCA). The dimension-reduced feature vectors were splitted into training set and test set. With training set images, Support Vector Machines (SVM) classifier was trained and the model parameters were defined. The classifier performance was evaluated with test set images and nearly 93% accuracy rate was achieved.
Feature vector
Contextual image classification
Standard test image
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Three-dimensional face recognition
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