Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly bene...Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly benefits several face analysis tasks,including ethnicity and race classification.We propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation model.For training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and mouth.The DCNN model developed in the first phase was used to create segmentation results.The probabilistic classification method is used,and probability maps(PMs)are created for each semantic class.We investigated five salient facial features from among seven that help in race classification.Features are extracted from the PMs of five classes,and a new model is trained based on the DCNN.We assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.展开更多
This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimiz...This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising,edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio(PSNR),signal-to-noise ratio(SNR),and structural similarity index(SSIM)corresponded to the current conventional TV-based schemes.展开更多
Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis ...Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.展开更多
Secure exchange of information is the basic need of modern digital world of e-communication which is achieved either by encrypting information or by hiding information in other information called cover media. Conceali...Secure exchange of information is the basic need of modern digital world of e-communication which is achieved either by encrypting information or by hiding information in other information called cover media. Concealing information requires a well designed technique of Stegnography. This work presents a technique, variable tone variable bits (VTVB) Stegnography, to hide information in a cover image. The VTVB Stegnography hides variable data in discrete cosine transform (DCT) coefficients of the cover image. VTVB Stegnography provides variable data hiding capacity and variable distortion. Additional large data hiding this technique provide extra security due to the large key size making VTVB Stegnography technique much more immune to steganalysis. The hiding makes the existence of information imperceptible for steganalysis and the key of keeping a secret makes the recovering of information difficult for an intruder. The key size is depending on cover image and numbers of bits of discrete cosine transform (DCT) coefficients used for information embedding. This is a very flexible technique and can be used for low payload applications, e.g. watermarking to high payload applications, e.g. network Stegnography.展开更多
This work presents a new method of data hiding in digital images,in discrete cosine transform domain.The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding...This work presents a new method of data hiding in digital images,in discrete cosine transform domain.The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding the secret information,while the low and high frequency coefficients are kept unaltered.The unaltered low frequency DCT coefficients preserves the quality of the smooth region of the cover image,while no changes in the high DCT coefficient preserve the quality of the edges.As the medium frequency components have less contribution towards energy and image details,so the modification of these coefficients for data hiding results in high quality stego images.The distortion due to the changes in the medium frequency coefficients is insignificant to be detected by the human visual system.The proposed methods demonstrated a hiding capacity of 43:11%with the stego image quality of a peak signal to the noise ration of 36:3 dB,which is significantly higher than the threshold of 30 dB for a stego image quality.The proposed technique is immune to steganalysis and has proved to be highly secured against both spatial and DCT domain steganalysis techniques.展开更多
Cryptography and steganography are two important and related fields of information security.But,steganography is slightly different in the sense that it hides the existence of secret information from unauthorized user...Cryptography and steganography are two important and related fields of information security.But,steganography is slightly different in the sense that it hides the existence of secret information from unauthorized users.It is one of the most appealing research domains,have applications like copyright protection,data integrity protection and manipulation detection.Several steganography techniques have been proposed in literature.But,in this work a new information hiding algorithm is presented.The proposed technique de-correlates frequency components of cover image using discrete cosine transform and uses the least significant bits of frequency components for hiding secret information.The tech-nique hides variable number of bits of secret message in different frequency components.Therefore,it hides different amount of secret information in different regions of cover im-age and results in enhancement of security.The algorithm has the flexibility to change the hiding capacity and quality of final stego image.It has been observed from experimental results that a hiding a capacity from 3%to 43%can be achieved with significantly good quality of 41 dB to 37 dB in term of peak signal to noise ratio.The successful recovery of the hidden information need the pattern,called stego key,in which is used in hiding process.The algorithm provides twofold security;hiding keeps the existence of hidden information secret and the large key size makes the retrieval of hidden information difficult for intruders.展开更多
基金This work was partially supported by a National Research Foundation of Korea(NRF)grant(No.2019R1F1A1062237)under the ITRC(Information Technology Research Center)support program(IITP-2021-2018-0-01431)supervised by the IITP(Institute for Information and Communications Technology Planning and Evaluation)funded by the Ministry of Science and ICT(MSIT),Korea.
文摘Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly benefits several face analysis tasks,including ethnicity and race classification.We propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation model.For training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and mouth.The DCNN model developed in the first phase was used to create segmentation results.The probabilistic classification method is used,and probability maps(PMs)are created for each semantic class.We investigated five salient facial features from among seven that help in race classification.Features are extracted from the PMs of five classes,and a new model is trained based on the DCNN.We assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.
文摘This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising,edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio(PSNR),signal-to-noise ratio(SNR),and structural similarity index(SSIM)corresponded to the current conventional TV-based schemes.
基金Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(2020-0-01592)Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant(2019R1F1A1058548)and Grant(2020R1G1A1013221).
文摘Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.
文摘Secure exchange of information is the basic need of modern digital world of e-communication which is achieved either by encrypting information or by hiding information in other information called cover media. Concealing information requires a well designed technique of Stegnography. This work presents a technique, variable tone variable bits (VTVB) Stegnography, to hide information in a cover image. The VTVB Stegnography hides variable data in discrete cosine transform (DCT) coefficients of the cover image. VTVB Stegnography provides variable data hiding capacity and variable distortion. Additional large data hiding this technique provide extra security due to the large key size making VTVB Stegnography technique much more immune to steganalysis. The hiding makes the existence of information imperceptible for steganalysis and the key of keeping a secret makes the recovering of information difficult for an intruder. The key size is depending on cover image and numbers of bits of discrete cosine transform (DCT) coefficients used for information embedding. This is a very flexible technique and can be used for low payload applications, e.g. watermarking to high payload applications, e.g. network Stegnography.
文摘This work presents a new method of data hiding in digital images,in discrete cosine transform domain.The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding the secret information,while the low and high frequency coefficients are kept unaltered.The unaltered low frequency DCT coefficients preserves the quality of the smooth region of the cover image,while no changes in the high DCT coefficient preserve the quality of the edges.As the medium frequency components have less contribution towards energy and image details,so the modification of these coefficients for data hiding results in high quality stego images.The distortion due to the changes in the medium frequency coefficients is insignificant to be detected by the human visual system.The proposed methods demonstrated a hiding capacity of 43:11%with the stego image quality of a peak signal to the noise ration of 36:3 dB,which is significantly higher than the threshold of 30 dB for a stego image quality.The proposed technique is immune to steganalysis and has proved to be highly secured against both spatial and DCT domain steganalysis techniques.
文摘Cryptography and steganography are two important and related fields of information security.But,steganography is slightly different in the sense that it hides the existence of secret information from unauthorized users.It is one of the most appealing research domains,have applications like copyright protection,data integrity protection and manipulation detection.Several steganography techniques have been proposed in literature.But,in this work a new information hiding algorithm is presented.The proposed technique de-correlates frequency components of cover image using discrete cosine transform and uses the least significant bits of frequency components for hiding secret information.The tech-nique hides variable number of bits of secret message in different frequency components.Therefore,it hides different amount of secret information in different regions of cover im-age and results in enhancement of security.The algorithm has the flexibility to change the hiding capacity and quality of final stego image.It has been observed from experimental results that a hiding a capacity from 3%to 43%can be achieved with significantly good quality of 41 dB to 37 dB in term of peak signal to noise ratio.The successful recovery of the hidden information need the pattern,called stego key,in which is used in hiding process.The algorithm provides twofold security;hiding keeps the existence of hidden information secret and the large key size makes the retrieval of hidden information difficult for intruders.
