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Talking Face Generation via Learning Semantic and Temporal Synchronous Landmarks

Talking Face Generation via Learning Semantic and Temporal S...

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international conference on pattern recognition

作者： Aihua Zheng Feixia Zhu Hao Zhu Mandi Luo Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Heifei China Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China

ISBN: (纸本)9781728188089;9781728188096

Given a speech clip and facial image, the goal of talking face generation is to synthesize a talking face video with accurate mouth synchronization and natural face motion. Recent progress has proven the effectiveness of the landmarks as the intermediate information during talking face generation. However, the large gap between audio and visual modalities makes the prediction of landmarks challenging and limits generation ability. This paper proposes a semantic and temporal synchronous landmark learning method for talking face generation. First, we propose to introduce a word detector to enforce richer semantic information. Then, we propose to preserve the temporal synchronization and consistency between landmarks and audio via the proposed temporal residual loss. Lastly, we employ a U-Net generation network with adaptive reconstruction loss to generate facial images for the predicted landmarks. Experimental results on two benchmark datasets LRW and GRID demonstrate the effectiveness of our model compared to the state-of-the-art methods of talking face generation.

关键词： Learning systems Visualization Face recognition Semantics Mouth Detectors Benchmark testing

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Plant Disease recognition and Crop Recommendation System using Deep Learning

Plant Disease Recognition and Crop Recommendation System usi...

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international conference on Computational Science and Technology (ICCST)

作者： Sreeraj. R Prayagdev. E Vishnu Murali Jaefer Akhilkumar K S Dept. of computer science and engineering Universal Engineering College Thrissur Kerala India

ISBN: (纸本)9781665476560

Agriculture is the foundation of our country. India, the world's second-largest producer of agricultural goods, generates over 280 million tonnes annually, which accounts for more than 15% of its GDP. A.A.R.C.S. (Advanced Agricultural Recommendation and Classification System) is a flutter-based farmer-friendly Mobile Application. One of the features of the proposed system is the crop recommendation feature that recommends correct crop by considering parameters like potassium, phosphorous and nitrogen contents, temperature, rainfall, and humidity. The second feature is seedling classification (using transfer learning), to identify the plants at an early stage. And as the third feature, the system also uses transfer learning to teach the model to detect common plant diseases. The above two models accept the image as an input and recognize and classify the image into the corresponding class. The proposed system uses edge-computing technology (Deep-Learning based model inside the mobile application) which simply means “taking action on data as near to the source as possible rather than in a central, remote data center, to reduce latency and bandwidth use”, i.e. the classification will be done on-device itself, thus overcoming the need of high latency and high bandwidth network links. The application also features a common platform to list the products of the farmers and tools and lands for rent service by providing the respective contact numbers of the owners. There will also be an encrypted community chat option which will be implemented using network sockets. The proposed system also contains an admin dashboard that provides infographics about users, common diseases, chat usage, push notification service and its analytics, etc. The mobile application will be the data source for disease-based analytics.

关键词： Productivity Plant diseases Scientific computing soft sensors Sockets Transfer learning Crops

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Online pattern recognition of Time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble

Online Pattern Recognition of Time-series Gas Sensor Data wi...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Vishakha Pareek Santanu Chaudhury Sanjay Singh CSIR - Central Electronics Engineering Research Institute Pilani India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India Indian Institute of Technology Delhi India

ISBN: (纸本)9781665426060

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. The presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. The responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. The Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. The network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. The proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.

关键词： Deep learning Gases Adaptive systems Neural networks Data acquisition pattern recognition Gas detectors

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Towards micro-expression recognition through pyramid of uniform temporal local binary pattern features 18th

Towards micro-expression recognition through pyramid of unif...

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Joint conferences on 18th international conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired computing , NaBIC 2018

作者： Ben Abdallah, Taoufik Guermazi, Radhouane Hammami, Mohamed Faculty of Economics and Management MIR@CL University of Sfax Sfax Tunisia Saudi Electronic University Riyadh Saudi Arabia Faculty of Sciences MIR@CL University of Sfax Sfax Tunisia

ISBN: (纸本)9783030166564

Compared to macro-expressions, recognizing micro-expres-sions is more challenging due to low intensity and their brief duration. To deal with this issue, the present paper proposes a facial micro-expression recognition approach based on the pyramid of uniform Temporal Local Binary pattern (PTLBPu2) features for describing the appearance motion changes in time through video stream. Unlike the majority of approaches that use a high dimensional feature space, the proposed approach is based on a low dimensional space with only 83 features. Compared to the most recent facial micro-expression recognition approaches, our approach proves its effectiveness with an accuracy rate reaching 66.40% on CasmeÂ II dataset. A study of the ability of a macro-expression model to recognize micro-expression shows that it is more efficient to recognize certain micro-expressions than others. © Springer Nature Switzerland AG 2020.

关键词： Local binary pattern

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Illustration and Identification of Skin Lesions in Dermoscopic Dataset Using pattern recognition Techniques 5th

Illustration and Identification of Skin Lesions in Dermoscop...

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5th international conference on Intelligent, Interactive Systems and Applications, IISA 2020

作者： Haider, Syeda Tooba Hussain, Syed Basharat Batool, Amreen Rokia, Laraba Selsabil Mohsan, Syed Agha Hassnain Sadiq, Muhammad Abubakar Saleem, Mazahir North China Electric Power University Beijing China Tiangong University Tianjin China East China University of Science and Technology Shanghai China Ocean College Zhejiang University Hangzhou China Beijing China Islamabad Pakistan

ISBN: (纸本)9783030637835

This paper proposes a generic method of description of skin lesion types and recognition of Melanocytic lesions in Dermoscopic images. Scientific judgments show that cancerous lesions demonstrate various characteristics such as streaks, texture or pigmented networks as compared to normal or benign lesions. Our aim is to confirm those judgments by carrying out proper segmentation, features extraction and classification. Segmentation phase of PH2 Public dataset was done by means of active contours. Features extraction stage included implementation of Local Binary patterns and its extension to study textural features more deeply. Finally, K nearest neighbor and Support vector machine were used for classification. This choice of methods is motivated by scientific importance of differential configurationally structures in the melanomas. The experiment of the offered algorithm is done on both sets of images, manually segmented (segmentation done by physicians followed by Features extraction and classification done by us) and automatic segmented images (all the steps performed by us) using performance metrics of sensitivity, specificity and Accuracy. The highest result of the system was attained by manually segmented images, i.e. SE = 89.2, SP = 84, Accuracy = 89.2. Whereas, images with automated segmentation showed SE = 89.3, SP = 75.4, Accuracy = 88.2. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Heatmap-Based Method for Estimating Drivers' Cognitive Distraction 19

Heatmap-Based Method for Estimating Drivers' Cognitive Distr...

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19th IEEE international conference on Cognitive Informatics and Cognitive computing (ICCI*CC)

作者： Musabini, Antonyo Chetitah, Mounsif InnoCoRe Valeo Comfort & Driving Assistance Bobigny France

ISBN: (纸本)9781728195940

In order to increase road safety, among the visual and manual distractions, modern intelligent vehicles need also to detect cognitive distracted driving (i.e., the driver's mind wandering). In this study, the influence of cognitive processes on the driver's gaze behavior is explored. A novel image-based representation of the driver's eye-gaze dispersion is proposed to estimate cognitive distraction. Data are collected on open highway roads, with a tailored protocol to create cognitive distraction. The visual difference of created shapes shows that a driver explores a wider area in neutral driving compared to distracted driving. Support vector machine (SVM)-based classifiers are trained, and 85.2% of accuracy is achieved for a two-class problem, even with a small dataset. Thus, the proposed method has the discriminative power to recognize cognitive distraction using gaze information. Finally, this work details how this image-based representation could he useful for other cases of distracted driving detection.

关键词： cognitive distraction distracted driving eyegaze affective computing human-centered artificial intelligence machine learning computer vision pattern recognition

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Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT Images 1

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1st 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with 23rd international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Chen, Haibin Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Perception Vision Medical Technology Guangzhou China

ISBN: (数字)9783030671945

ISBN: (纸本)9783030671938

The automatic segmentation of head and neck (H&N) tumor from FDG-PET and CT images is urgently needed for radiomics. In this paper, we propose a framework to segment H&N tumor automatically by fusing information of PET and CT. In this framework, multiple 3D-Unets are trained one-by-one. The predictions and features of upstream models will be captured as additional information for the next one to further refine the segmentation. Experiments show that iterative refinements can improve the performance. We evaluated our framework on the dataset of HECKTOR2020 (The challenge of head and neck tumor segmentation) and won the 5th place with average DSC, precision and recall of 0.7241, 0.8479, 0.6701 respectively. © 2021, Springer Nature Switzerland AG.

关键词： Computerized tomography

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Predicting the Cricket Match Outcome Using ANFIS Classifier for Viewers Opinions on Twitter Data 4th

Predicting the Cricket Match Outcome Using ANFIS Classifier ...

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4th international conference on Data Management, Analytics and Innovation, ICDMAI 2020

作者： Anbazhagu, U.V. Anandan, R. Chennai600117 India

ISBN: (纸本)9789811556159

Fortunately or unfortunately the new era of web-dependent life has made our decision-making to start from the web ranking;one can express sentiments with opportunity. The decision is additionally examined via web-based networking media, for example, Twitter. In this article, endeavors are made to examine the practicality of utilizing aggregate learning acquired micro post presented on Twitter to anticipate the victor of a Cricket coordinate. For forecasts, we utilize three unique strategies that rely upon the all-out tweet behind the amusement for group, fan assessments toward group, and fans score expectations on twitter. By joining these strategies, we arrange winning group expectation in a Cricket diversion before the beginning of amusement. Our outcomes are sufficiently promising to be utilized for winning group figure. Besides, the adequacy of Adaptive Neuro-Fuzzy Interference System (ANFIS) is to recover tweets from Twitter, examine deliberately, and draw a precise outcome dependent on its inspiration and cynicism. In this, we have displayed an execution of unsupervised technique for nostalgic investigation for IPL 2019 match. Here we can anticipate the result of cricket coordinate before its initiation, in light of the tweets shared by their fans utilizing Twitter. © 2021, Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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Effect of Feature selection on the performance of Liver Tumor classification using Neural Networks

Effect of Feature selection on the performance of Liver Tumo...

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Communication, computing and Industry 4.0 (C2I4), international conference on

作者： Munipraveena Rela Nagaraja Rao Suryakari P. Ramana Reddy Jawaharlal Nehru Technological University Ananthapuramu Ananthapur A P India G. Pulla Reddy Engineering College (Autonomous) Kurnool A P India JNTUA College of Engineering (Autonomous) Ananthapuramu Kurnool A P India

ISBN: (纸本)9781665420143

Neural network designs have pushed the boundaries of clinical picture analysis achieving remarkable performance in responsibilities together with tissue category and segmentation as well as prediction of numerous scientific consequences. There are different kinds of cancers happening in the liver. Various growths have diverse pictorial appearances and this changes after infusion of the differentiation medium. Hence, finding the location of liver malignancies is a difficult undertaking. In this paper, we are going to discuss liver tumor classification using neural networks. Here we have used a pattern recognition network, with a training function as Scaled Conjugate Gradient, hidden neurons are 10, performance function as cross-entropy. Here we have collected 67 CT image which includes both Liver abscess and Hepatocellular carcinoma. 93 features were extracted from these images for training, validation, and test of the network. By including GLRLM features, the mean square error is reduced by 28.31%.

关键词： Training Neurons Liver Mean square error methods Feature extraction pattern recognition Telecommunication computing

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international conference on Cognitive Informatics and soft computing, CISC 2019

International Conference on Cognitive Informatics and Soft C...

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international conference on Cognitive Informatics and soft computing, CISC 2019

ISBN: (纸本)9789811514500

The proceedings contain 69 papers. The special focus in this conference is on Cognitive Informatics and soft computing. The topics include: Detection and Prediction of Schizophrenia Using Magnetic Resonance Images and Deep Learning;Intrusion Detection Systems (IDS)—An Overview with a Generalized Framework;hybrid Machine Learning Model for Context-Aware Social IoT Using Location-Based Service Under Both Static and Mobility Conditions;an Efficient Fuzzy Logic Control-Based soft computing Technique for Grid-Tied Photovoltaic System;prediction of Academic Performance of Alcoholic Students Using Data Mining Techniques;decision Support System for Determining Academic Advisor Using Simple Additive Weighting;analysis of Human Serum and Whole Blood for Transient Biometrics Using Minerals in the Human Body;Design and Implementation of Animal Activity Monitoring System Using TI Sensor Tag;improved Filtering of Noisy Images by Combining Average Filter with Bacterial Foraging Optimization Technique;detection of Epileptic Seizure Based on ReliefF Algorithm and Multi-support Vector Machine;accessing Sensor Data via Hybrid Virtual Private Network Using Multiple Layer Encryption Method in Big Data;statistical Features-Based Violence Detection in Surveillance Videos;encapsulated Features with Multi-objective Deep Belief Networks for Action Classification;LU/LC Change Detection Using NDVI & MLC Through Remote Sensing and GIS for Kadapa Region;text Region Extraction for Noisy Spam Image;noise Reduction in Lidar Signal Based on Sparse Difference Method;iris recognition System Based on Lifting Wavelet;classification of Abusive Comments Using Various Machine Learning Algorithms;simulation of Web Service Selection from a Web Service Repository;blockchain-Based Shared Security Architecture;issues of Bot Network Detection and Protection.

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