To overcome these deficiencies can be use a three-dimensional ( 3D ) face recognition system . This research did three-dimensional ( 3D ) face recognition by not doing 3D face reconstruction . But using face data got from camera ToF i.e. distance , amplitude , and intensity from each image pixel as input data . In this work we evaluate the influence of pose variation on 3D face reconstruction from a single image . To this end , we present a 3D reconstruction method that combines a fitting technique and a sparse 3D deformable model to estimate the 3D information of 2D images with large pose variations . OF DISSERTATION 3D Human Face Reconstruction and 2D Appearance Synthesis 3D human face reconstruction has been an extensive research for decades due to its wide applications , such as animation , recognition and 3D-driven appearance synthesis . Although commodity depth sensors are widely available in recent years , image based face reconstruction are significantly valuable as images are much easier to access and store . The 3D reconstruction of a face from a single frontal image is an ill-posed problem . This is further accentuated when the face image is captured under different poses and/or complex illumination conditions . Among the local algorithms relying on the fronto-parallel assumption , our algorithm is the best ranked algorithm . We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face . To this end , we propose a method for reconstructing a frontal face from a non-fontal face in a single image . Detected 2D fiducial points-based 3D Morphable Model ( 3DMM ) fitting used for 3D face shape reconstruction . Frontal-view 3DMM is generated by rotating non-frontal-view 3DMM . The spherical harmonics ( SH ) model provides a compact basis to well approximate the image appearance of a Lambertian object under different illumination conditions . Recently , the SH and morphable models have been integrated for 3D face shape reconstruction . However , the reconstructed 3D shape is either inconsistent with the SH bases or obtained just from landmarks only . Using 3D information in facial recognition can improve the performance of facial recognition over conventional 2D facial recognition . To perform 3D facial recognition , a 3D reconstruction of the face has to be available . Because most commercially available 3D scanning solutions that have a sufﬁcient accuracy for performing facial recognition are often expensive , it was decided to make an open-source 3D laser-line scanner that can be used for facial acquisition . This study evaluates five feature detectors popular used in 3-dimensional face recognition systems , where face images quality is improved by removing high frequencies at the Discrete Cosine Transform coefficient domain , because at this frequency contained noise . The evaluation is important to be done because the feature detector functions to look for keypoints on the face image needed in 3-dimensional face reconstruction from 2 dimensional face images , and th e accuracy of 3d face recognition depends on the success of the reconstruction . Several researches had been undertaken in 3D face recognition , among others by implementing feature detector to determine the same key point on two face images 2 dimensional at different angles to simulate epipolar geometry . However , most existing 3D face reconstruction methods suffer from complicated processing and high computation . This paper presents a novel method that simplifies the 3D face reconstruction process with only one shot of Kinect data . The output of the system is a high density of 3D face point cloud with smoother surface . Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D - Assisted Self - Supervised Learning section : Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network , the attribute-guided network becomes the identity-guided conditional CycleGAN which produces high-quality and interesting results on identity transfer . We demonstrate three applications on identity-guided conditional CycleGAN : identity-preserving face superresolution , face swapping , and frontal face generation , which consistently show the advantage of our new method . This limits its applications . This paper presents a method for face swapping based on personalized 3D head models . This framework builds a personalized 3D head model from a frontal face and can be rendered at any pose to match the characters in the image we want to swap . A realistic visualization is achieved using an adaptive texture sampling technique . The face swap is performed irrespective of the sex , age or ethnicity of the subject in front of the camera . This application is intended for gaming , shopping , educational or entertainment purposes and will be presented in a real-time setup during the demo session . Video facial swap usually has strong entertainment applications , and it is also applicable for the post-production of films and has great application value . At present , the popular face swap is done manually by the PS software , and the synthetic effect of the automatic face changing technology is not good . In order to make up for the lack of these features , this paper proposes a method of video face swap based on autoencoder generation network . This survey provides a thorough review of techniques for manipulating face images including DeepFake methods , and methods to detect such manipulations . In particular , four types of facial manipulation are reviewed : i ) entire face synthesis , ii ) face identity swap ( DeepFakes ) , iii ) facial attributes manipulation , and iv ) facial expression manipulation . For each manipulation type , we provide details regarding manipulation techniques , existing public databases , and key benchmarks for technology evaluation of fake detection methods , including a summary of results from those evaluations . Among the different databases available and discussed in the survey , FaceForensics++ is for example one of the most widely used for detecting both face identity swap and facial expression manipulations , with results in the literature in the range of 90 - 100 % of manipulation detection accuracy . In addition to the survey information , we also discuss trends and provide an outlook of the ongoing work in this field , e.g. , the recently announced DeepFake Detection Challenge ( DFDC ) . While such enablements have positive effects , as a negative side , now anyone can digitally attack face ( biometric ) recognition systems . For example , face swapping application of Snapchat can easily create “ swapped ” identities and circumvent face recognition system . This research presents a novel database , termed as SWAPPED — Digital Attack Video Face Database , prepared using Snap chat 's application which swaps/stitches two faces and creates videos . The images from two sets are unstructured collection of people 's photographs . This approach is enabled by framing the face swapping problem in terms of style transfer , where the goal is to render an image in the style of another one . We design a new autoencoder network that enables the processing to produce photorealistic results . This abstract introduces a generative neural network for face swapping and editing face images . We refer to this network as " region-separative generative adversarial network ( RSGAN ) " . The output of one branch of the decoder is used for segmenting the manipulated regions while that of the other branch is used for reconstructing the input , which helps improve overall performance . Experiments using the FaceForensics and Face-Forensics++ databases demonstrated the networks effectiveness against facial reenactment attacks and face swapping attacks as well as its ability to deal with the mismatch condition for previously seen attacks . Moreover , fine-tuning using just a small amount of data enables the network to deal with unseen attacks . Unfortunately , this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent . In this study , we use deep transfer learning for face swapping detection , showing true positive rates greater than 96 % with very few false alarms . Distinguished from existing methods that only provide detection accuracy , we also provide uncertainty for each prediction , which is critical for trust in the deployment of such detection systems . We have proposed a new feature extraction method and a new feature fusion strategy based on generalized canonical correlation analysis ( GCCA ) . The proposed method and strategy have been applied to facial feature extraction and recognition . Compared with the face feature extracted by canonical correlation analysis ( CCA ) , as in a process of GCCA , it contains the class information of the training samples , thus , aiming for pattern classification it would improve the classification capability . The direction of movement is used to filter out irrelevant , macro movements . The proposed algorithm for micro-expression detection is training free and it involves a simple voting procedure on the movement magnitude from each region . The proposed method was tested on a publicly available database . However , little progress has been made in spotting micro-expressions . In this paper , we propose a Main Directional Maximal Difference ( MDMD ) Analysis for micro-expression spotting . MDMD uses the magnitude maximal difference in the main direction of optical flow features to spot facial movements , including micro-expressions . Although methods for the spotting and recognition of general facial expressions have been investigated , little progress has been made in the automatic spotting and recognition of micro-expressions . In this paper , we proposed the Main Directional Maximal Difference ( MDMD ) analysis for micro-expression spotting . MDMD uses the magnitude of maximal difference in the main direction of optical flow as a feature to spot facial movements , including micro-expressions . Microexpression recognition in videos has important applications in psychotherapy , forensics , homeland security and negotiation , among others , for providing significant clues to hidden emotion detection . Since these expressions are very brief and difficult to be detected by the naked eye , automatic recognition is a natural step forward in the field . Intensive experiments are conducted on two available published micro-expression databases including SMIC and CASME2 databases for evaluating the performance of the proposed method . Experimental results demonstrate that the proposed method achieves promising performance in microexpression recognition . Automatic detection of micro-expression from a video is the first step in the micro-expression analysis . In this paper , we present a method addressing the micro-expression detection problem based on the differences in the Integral Projection ( IP ) of sequential frames . The method can detect the temporal location of the micro-expression . The authors present the problem of image analysis for the purpose of identifying emotions . In addition , they point out the importance of recording the phenomenon of the development of emotions on the human face with the use of high-speed cameras , which allows the detection of micro expression . The work that was prepared for this article was based on analyzing the parallax pair correlation coefficients for specific faces . In this paper , we focus on AU detection in micro-expressions . Microexpression AU detection is challenging due to the small quantity of micro-expression databases , low intensity , short duration of facial muscle change , and class imbalance . In order to alleviate the problems , we propose a novel Spatio-Temporal Adaptive Pooling ( STAP ) network for AU detection in micro-expressions . Micro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings . Spotting micro-expression means to locate the moment when the micro-expression happens , which is a primary step for micro-expression recognition . Previous work in micro-expression spotting focus on spotting micro-expression from short video , and with hand-crafted features . This motivated us to propose a novel joint architecture of spatial and temporal network which extracts time-contrasted features from the feature maps to contrast out micro-expression from rapid muscle movements . The usage of time contrasted features greatly improved the spotting of micro-expression from inconspicuous facial movements . Also , we include a memory module to predict the class and intensity of the micro-expression across the temporal frames of the micro-expression clip .
To overcome these deficiencies can be use a three-dimensional ( 3D ) face recognition system . This research did three-dimensional ( 3D ) face recognition by not doing 3D face reconstruction . But using face data got from camera ToF i.e. distance , amplitude , and intensity from each image pixel as input data .
In this work we evaluate the influence of pose variation on 3D face reconstruction from a single image . To this end , we present a 3D reconstruction method that combines a fitting technique and a sparse 3D deformable model to estimate the 3D information of 2D images with large pose variations .
OF DISSERTATION 3D Human Face Reconstruction and 2D Appearance Synthesis 3D human face reconstruction has been an extensive research for decades due to its wide applications , such as animation , recognition and 3D-driven appearance synthesis . Although commodity depth sensors are widely available in recent years , image based face reconstruction are significantly valuable as images are much easier to access and store .
The 3D reconstruction of a face from a single frontal image is an ill-posed problem . This is further accentuated when the face image is captured under different poses and/or complex illumination conditions .
Among the local algorithms relying on the fronto-parallel assumption , our algorithm is the best ranked algorithm . We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face .
To this end , we propose a method for reconstructing a frontal face from a non-fontal face in a single image . Detected 2D fiducial points-based 3D Morphable Model ( 3DMM ) fitting used for 3D face shape reconstruction . Frontal-view 3DMM is generated by rotating non-frontal-view 3DMM .
The spherical harmonics ( SH ) model provides a compact basis to well approximate the image appearance of a Lambertian object under different illumination conditions . Recently , the SH and morphable models have been integrated for 3D face shape reconstruction . However , the reconstructed 3D shape is either inconsistent with the SH bases or obtained just from landmarks only .
Using 3D information in facial recognition can improve the performance of facial recognition over conventional 2D facial recognition . To perform 3D facial recognition , a 3D reconstruction of the face has to be available . Because most commercially available 3D scanning solutions that have a sufﬁcient accuracy for performing facial recognition are often expensive , it was decided to make an open-source 3D laser-line scanner that can be used for facial acquisition .
This study evaluates five feature detectors popular used in 3-dimensional face recognition systems , where face images quality is improved by removing high frequencies at the Discrete Cosine Transform coefficient domain , because at this frequency contained noise . The evaluation is important to be done because the feature detector functions to look for keypoints on the face image needed in 3-dimensional face reconstruction from 2 dimensional face images , and th e accuracy of 3d face recognition depends on the success of the reconstruction . Several researches had been undertaken in 3D face recognition , among others by implementing feature detector to determine the same key point on two face images 2 dimensional at different angles to simulate epipolar geometry .
However , most existing 3D face reconstruction methods suffer from complicated processing and high computation . This paper presents a novel method that simplifies the 3D face reconstruction process with only one shot of Kinect data . The output of the system is a high density of 3D face point cloud with smoother surface .
Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D - Assisted Self - Supervised Learning section :
Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network , the attribute-guided network becomes the identity-guided conditional CycleGAN which produces high-quality and interesting results on identity transfer . We demonstrate three applications on identity-guided conditional CycleGAN : identity-preserving face superresolution , face swapping , and frontal face generation , which consistently show the advantage of our new method .
This limits its applications . This paper presents a method for face swapping based on personalized 3D head models . This framework builds a personalized 3D head model from a frontal face and can be rendered at any pose to match the characters in the image we want to swap .
A realistic visualization is achieved using an adaptive texture sampling technique . The face swap is performed irrespective of the sex , age or ethnicity of the subject in front of the camera . This application is intended for gaming , shopping , educational or entertainment purposes and will be presented in a real-time setup during the demo session .
Video facial swap usually has strong entertainment applications , and it is also applicable for the post-production of films and has great application value . At present , the popular face swap is done manually by the PS software , and the synthetic effect of the automatic face changing technology is not good . In order to make up for the lack of these features , this paper proposes a method of video face swap based on autoencoder generation network .
This survey provides a thorough review of techniques for manipulating face images including DeepFake methods , and methods to detect such manipulations . In particular , four types of facial manipulation are reviewed : i ) entire face synthesis , ii ) face identity swap ( DeepFakes ) , iii ) facial attributes manipulation , and iv ) facial expression manipulation . For each manipulation type , we provide details regarding manipulation techniques , existing public databases , and key benchmarks for technology evaluation of fake detection methods , including a summary of results from those evaluations . Among the different databases available and discussed in the survey , FaceForensics++ is for example one of the most widely used for detecting both face identity swap and facial expression manipulations , with results in the literature in the range of 90 - 100 % of manipulation detection accuracy . In addition to the survey information , we also discuss trends and provide an outlook of the ongoing work in this field , e.g. , the recently announced DeepFake Detection Challenge ( DFDC ) .
While such enablements have positive effects , as a negative side , now anyone can digitally attack face ( biometric ) recognition systems . For example , face swapping application of Snapchat can easily create “ swapped ” identities and circumvent face recognition system . This research presents a novel database , termed as SWAPPED — Digital Attack Video Face Database , prepared using Snap chat 's application which swaps/stitches two faces and creates videos .
The images from two sets are unstructured collection of people 's photographs . This approach is enabled by framing the face swapping problem in terms of style transfer , where the goal is to render an image in the style of another one . We design a new autoencoder network that enables the processing to produce photorealistic results .
This abstract introduces a generative neural network for face swapping and editing face images . We refer to this network as " region-separative generative adversarial network ( RSGAN ) " .
The output of one branch of the decoder is used for segmenting the manipulated regions while that of the other branch is used for reconstructing the input , which helps improve overall performance . Experiments using the FaceForensics and Face-Forensics++ databases demonstrated the networks effectiveness against facial reenactment attacks and face swapping attacks as well as its ability to deal with the mismatch condition for previously seen attacks . Moreover , fine-tuning using just a small amount of data enables the network to deal with unseen attacks .
Unfortunately , this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent . In this study , we use deep transfer learning for face swapping detection , showing true positive rates greater than 96 % with very few false alarms . Distinguished from existing methods that only provide detection accuracy , we also provide uncertainty for each prediction , which is critical for trust in the deployment of such detection systems .
We have proposed a new feature extraction method and a new feature fusion strategy based on generalized canonical correlation analysis ( GCCA ) . The proposed method and strategy have been applied to facial feature extraction and recognition . Compared with the face feature extracted by canonical correlation analysis ( CCA ) , as in a process of GCCA , it contains the class information of the training samples , thus , aiming for pattern classification it would improve the classification capability .
The direction of movement is used to filter out irrelevant , macro movements . The proposed algorithm for micro-expression detection is training free and it involves a simple voting procedure on the movement magnitude from each region . The proposed method was tested on a publicly available database .
However , little progress has been made in spotting micro-expressions . In this paper , we propose a Main Directional Maximal Difference ( MDMD ) Analysis for micro-expression spotting . MDMD uses the magnitude maximal difference in the main direction of optical flow features to spot facial movements , including micro-expressions .
Although methods for the spotting and recognition of general facial expressions have been investigated , little progress has been made in the automatic spotting and recognition of micro-expressions . In this paper , we proposed the Main Directional Maximal Difference ( MDMD ) analysis for micro-expression spotting . MDMD uses the magnitude of maximal difference in the main direction of optical flow as a feature to spot facial movements , including micro-expressions .
Microexpression recognition in videos has important applications in psychotherapy , forensics , homeland security and negotiation , among others , for providing significant clues to hidden emotion detection . Since these expressions are very brief and difficult to be detected by the naked eye , automatic recognition is a natural step forward in the field .
Intensive experiments are conducted on two available published micro-expression databases including SMIC and CASME2 databases for evaluating the performance of the proposed method . Experimental results demonstrate that the proposed method achieves promising performance in microexpression recognition .
Automatic detection of micro-expression from a video is the first step in the micro-expression analysis . In this paper , we present a method addressing the micro-expression detection problem based on the differences in the Integral Projection ( IP ) of sequential frames . The method can detect the temporal location of the micro-expression .
The authors present the problem of image analysis for the purpose of identifying emotions . In addition , they point out the importance of recording the phenomenon of the development of emotions on the human face with the use of high-speed cameras , which allows the detection of micro expression . The work that was prepared for this article was based on analyzing the parallax pair correlation coefficients for specific faces .
In this paper , we focus on AU detection in micro-expressions . Microexpression AU detection is challenging due to the small quantity of micro-expression databases , low intensity , short duration of facial muscle change , and class imbalance . In order to alleviate the problems , we propose a novel Spatio-Temporal Adaptive Pooling ( STAP ) network for AU detection in micro-expressions .
Micro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings . Spotting micro-expression means to locate the moment when the micro-expression happens , which is a primary step for micro-expression recognition . Previous work in micro-expression spotting focus on spotting micro-expression from short video , and with hand-crafted features .
This motivated us to propose a novel joint architecture of spatial and temporal network which extracts time-contrasted features from the feature maps to contrast out micro-expression from rapid muscle movements . The usage of time contrasted features greatly improved the spotting of micro-expression from inconspicuous facial movements . Also , we include a memory module to predict the class and intensity of the micro-expression across the temporal frames of the micro-expression clip .
Security system with 3 dimensional face recognition using PCA method and neural networks algorithm Improving 3D face reconstruction from a single image using half-frontal face poses 3D Human Face Reconstruction and 2D Appearance Synthesis Learning 3D shape from a single facial image via non-linear manifold embedding and alignment Local Stereo Matching Using Adaptive Local Segmentation Frontal face reconstruction with symmetric constraints Reconstructing 3D Shape, Albedo and Illumination from a Single Face Image A low cost 3D laser-line scanner for facial acquisitio Removing DCT High Frequency on Feature Detector Repeatability Quality Automatic Reconstruction of Dense 3D Face Point Cloud with a Single Depth Image Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning Attribute-Guided Face Generation Using Conditional CycleGAN Face Swapping under Large Pose Variations: A 3D Model Based Approach Who do you want to be? Real-time face swap Video Face Swap Based on Autoencoder Generation Network DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern A Light Autoencoder Networks for Face Swapping RSGAN: face swapping and editing using face and hair representation in latent spaces Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos Swapped face detection using deep learning and subjective assessment Face Recognition Based on Generalized Canonical Correlation Analysis Micro-Expressions Detection Based on Micro-Motions Dense Optical Flows A main directional maximal difference analysis for spotting facial movements from long-term videos A Main Directional Maximal Difference Analysis for Spotting Micro-expressions Facial microexpression recognition based on descriptor and classifier combinations = Reconhecimento de microexpressões faciais baseado em combinações de descritores e classificadores Spontaneous facial micro-expression analysis using spatiotemporal local radon-based binary pattern Micro-expression detection using integral projections Emotion Recognition - the need for a complete analysis of the phenomenon of expression formation Micro-expression Action Unit Detection withSpatio-temporal Adaptive Pooling SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos Facial Micro-expression Spotting and Recognition Using Time Contrasted Feature with Visual Memory
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