This article proposes a novel framework for the recognition of six universal facial expressions.The framework is based on three sets of features extracted from a face image:entropy,brightness,and local binary pattern....This article proposes a novel framework for the recognition of six universal facial expressions.The framework is based on three sets of features extracted from a face image:entropy,brightness,and local binary pattern.First, saliency maps are obtained using the state-of-the-art saliency detection algorithm "frequency-tuned salient region detection".The idea is to use saliency maps to determine appropriate weights or values for the extracted features (i.e.,brightness and entropy).We have performed a visual experiment to validate the performance of the saliency detection algorithm against the human visual system.Eye movements of 15 subjects were recorded using an eye-tracker in free-viewing conditions while they watched a collection of 54 videos selected from the Cohn-Kanade facial expression database.The results of the visual experiment demonstrated that the obtained saliency maps are consistent with the data on human fixations.Finally,the performance of the proposed framework is demonstrated via satisfactory classification results achieved with the Cohn-Kanade database,FG-NET FEED database, and Dartmouth database of children's faces.展开更多
In order to solve the problem of poor teaching quality caused by classroom teachers’inability to grasp students’dynamics in time,this paper designs a feedback system for classroom attention with the help of the rese...In order to solve the problem of poor teaching quality caused by classroom teachers’inability to grasp students’dynamics in time,this paper designs a feedback system for classroom attention with the help of the research on expression recognition technology in deep learning.In the real-time analysis of expression,although the deeper deep learning network has more accurate recognition effect,there are drawbacks of too large model and too many parameters in the network training process.In this paper,we propose a student concentration algorithm that uses the Convolutional Block attentional module(CBAM)and the Local Binary Pattern(LBP)to reduce the number of parameters in the model by replacing the convolution with the depthseparable convolution LBP preprocessing enhances the feature validity of the input feature map and improves the training speed and accuracy of the model.The experimental results show that the algorithm has a good discriminating effect on expression recognition,and the model is small.展开更多
In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective...In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.展开更多
A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided ...A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.展开更多
Objective:To explore the possible correlation between traditional Chinese medicine(TCM)constitution and facial features in color images and to improve the accuracy of automated constitution classification.Methods:Colo...Objective:To explore the possible correlation between traditional Chinese medicine(TCM)constitution and facial features in color images and to improve the accuracy of automated constitution classification.Methods:Color images were taken of 5150 individuals of different professions.Automated face detection and key point positioning were performed on the collected images,which were then transformed into a standard size.The relationship between facial features and TCM constitution based on the red,green,blue(RGB)pixel and the local binary pattern(LBP)texture features was explored.Results:The overall accuracy rate and robustness of TCM constitution classification based on RGB features were low.Classification results of the phlegm-dampness,damp-heat,blood stasis,and balance constitutions achieved high accuracy rates.Classification accuracy rate using the LBP texture feature was higher than that of the RGB feature,with the best accuracy observed for the balance constitution.Conclusion:Application of computer image acquisition and processing of facial features may serve as an adjunct to the TCM diagnostic method of inspection.展开更多
Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians ar...Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.展开更多
文摘This article proposes a novel framework for the recognition of six universal facial expressions.The framework is based on three sets of features extracted from a face image:entropy,brightness,and local binary pattern.First, saliency maps are obtained using the state-of-the-art saliency detection algorithm "frequency-tuned salient region detection".The idea is to use saliency maps to determine appropriate weights or values for the extracted features (i.e.,brightness and entropy).We have performed a visual experiment to validate the performance of the saliency detection algorithm against the human visual system.Eye movements of 15 subjects were recorded using an eye-tracker in free-viewing conditions while they watched a collection of 54 videos selected from the Cohn-Kanade facial expression database.The results of the visual experiment demonstrated that the obtained saliency maps are consistent with the data on human fixations.Finally,the performance of the proposed framework is demonstrated via satisfactory classification results achieved with the Cohn-Kanade database,FG-NET FEED database, and Dartmouth database of children's faces.
基金Key research Project of higher education institutions in Henan Province(Project:Name:A Study on Students’concentration in Class Based on Deep Multi-task Learning Framework,Project No.23B413004).
文摘In order to solve the problem of poor teaching quality caused by classroom teachers’inability to grasp students’dynamics in time,this paper designs a feedback system for classroom attention with the help of the research on expression recognition technology in deep learning.In the real-time analysis of expression,although the deeper deep learning network has more accurate recognition effect,there are drawbacks of too large model and too many parameters in the network training process.In this paper,we propose a student concentration algorithm that uses the Convolutional Block attentional module(CBAM)and the Local Binary Pattern(LBP)to reduce the number of parameters in the model by replacing the convolution with the depthseparable convolution LBP preprocessing enhances the feature validity of the input feature map and improves the training speed and accuracy of the model.The experimental results show that the algorithm has a good discriminating effect on expression recognition,and the model is small.
文摘In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.
文摘A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.
基金the National Basic Research Program of China(973 Program,No.2011CB505404)National Twelfth Five-Year Plan for Science&Technology Support(No.2012BA125B05)China Postdoctoral Science Foundation(No.2014M560923).
文摘Objective:To explore the possible correlation between traditional Chinese medicine(TCM)constitution and facial features in color images and to improve the accuracy of automated constitution classification.Methods:Color images were taken of 5150 individuals of different professions.Automated face detection and key point positioning were performed on the collected images,which were then transformed into a standard size.The relationship between facial features and TCM constitution based on the red,green,blue(RGB)pixel and the local binary pattern(LBP)texture features was explored.Results:The overall accuracy rate and robustness of TCM constitution classification based on RGB features were low.Classification results of the phlegm-dampness,damp-heat,blood stasis,and balance constitutions achieved high accuracy rates.Classification accuracy rate using the LBP texture feature was higher than that of the RGB feature,with the best accuracy observed for the balance constitution.Conclusion:Application of computer image acquisition and processing of facial features may serve as an adjunct to the TCM diagnostic method of inspection.
文摘Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.
