MACHINE VISION GROUP Face image mapping from NIR to VIS Jie Chen Machine Vision Group

Slides:

Advertisements

Similar presentations

The Extended Cohn-Kanade Dataset(CK+):A complete dataset for action unit and emotion-specified expression Author：Patrick Lucey, Jeffrey F. Cohn, Takeo.

Advertisements

Matthias Wimmer, Ursula Zucker and Bernd Radig Chair for Image Understanding Computer Science Technische Universität München { wimmerm, zucker, radig

Salvatore giorgi Ece 8110 machine learning 5/12/2014

Face Alignment with Part-Based Modeling

HYBRID-BOOST LEARNING FOR MULTI-POSE FACE DETECTION AND FACIAL EXPRESSION RECOGNITION Hsiuao-Ying ChenChung-Lin Huang Chih-Ming Fu Pattern Recognition,

Adviser：Ming-Yuan Shieh Student：shun-te chuang SN：M

Watching Unlabeled Video Helps Learn New Human Actions from Very Few Labeled Snapshots Chao-Yeh Chen and Kristen Grauman University of Texas at Austin.

IEEE TCSVT 2011 Wonjun Kim Chanho Jung Changick Kim

Coherency Sensitive Hashing (CSH) Simon Korman and Shai Avidan Dept. of Electrical Engineering Tel Aviv University ICCV2011 | 13th International Conference.

Automatic Pose Estimation of 3D Facial Models Yi Sun and Lijun Yin Department of Computer Science State University of New York at Binghamton Binghamton,

Feature Detection and Emotion Recognition Chris Matthews Advisor: Prof. Cotter.

CSCE 641 Computer Graphics: Image-based Rendering (cont.) Jinxiang Chai.

CSCE 641: Computer Graphics Image-based Rendering Jinxiang Chai.

Image Denoising and Inpainting with Deep Neural Networks Junyuan Xie, Linli Xu, Enhong Chen School of Computer Science and Technology University of Science.

Predicting Matchability - CVPR 2014 Paper -

EKMAN’S FACIAL EXPRESSIONS STUDY A Demonstration.

Recognizing Emotions in Facial Expressions

以區域二元圖樣與部分比對為基礎之人臉辨識 Face Recognition with Local Binary Patterns and Partial Matching Presenter : 施佩汝 Advisor : 歐陽明教授 1.

Matthew Brown University of British Columbia (prev.) Microsoft Research [ Collaborators: † Simon Winder, *Gang Hua, † Rick Szeliski † =MS Research, *=MS.

Mood of Visual Images Objective: Identify and create the mood represented by a visual image.

Emotion Recognition using the GSR Signal on Android Devices Shuangjiang Li.

Unsupervised object discovery via self-organisation Presenter : Bo-Sheng Wang Authors: Teemu Kinnunen, Joni-Kristian Kamarainen, Lasse Lensu, Heikki Kälviäinen.

Face Alignment Using Cascaded Boosted Regression Active Shape Models

Human Emotion Synthesis David Oziem, Lisa Gralewski, Neill Campbell, Colin Dalton, David Gibson, Barry Thomas University of Bristol, Motion Ripper, 3CR.

Person-Specific Domain Adaptation with Applications to Heterogeneous Face Recognition (HFR) Presenter: Yao-Hung Tsai Dept. of Electrical Engineering, NTU.

Learning Visual Similarity Measures for Comparing Never Seen Objects By: Eric Nowark, Frederic Juric Presented by: Khoa Tran.

1 Facial Expression Recognition using KCCA with Combining Correlation Kernels and Kansei Information Yo Horikawa Kagawa University, Japan.

Music Emotion Recognition 許博智謝承諺.

3D Fingertip and Palm Tracking in Depth Image Sequences

KinectFusion : Real-Time Dense Surface Mapping and Tracking IEEE International Symposium on Mixed and Augmented Reality 2011 Science and Technology Proceedings.

Multimodal Information Analysis for Emotion Recognition

Just Noticeable Difference Estimation For Images with Structural Uncertainty WU Jinjian Xidian University.

Intelligent Control and Automation, WCICA 2008.

Optimal Dimensionality of Metric Space for kNN Classification Wei Zhang, Xiangyang Xue, Zichen Sun Yuefei Guo, and Hong Lu Dept. of Computer Science &

A NOVEL METHOD FOR COLOR FACE RECOGNITION USING KNN CLASSIFIER

Image Emotional Semantic Query Based On Color Semantic Description Wei-Ning Wang, Ying-Lin Yu Department of Electronic and Information Engineering, South.

Timo Ahonen, Abdenour Hadid, and Matti Pietikainen

Presenter : Jia-Hao Syu

Demosaicking for Multispectral Filter Array (MSFA)

Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.

Face Recognition Summary –Single pose –Multiple pose –Principal components analysis –Model-based recognition –Neural Networks.

Single Image Interpolation via Adaptive Non-Local Sparsity-Based Modeling The research leading to these results has received funding from the European.

Ekman’s Facial Expressions Study A Demonstration.

Cell Segmentation in Microscopy Imagery Using a Bag of Local Bayesian Classifiers Zhaozheng Yin RI/CMU, Fall 2009.

Facial Expressions and Emotions Mental Health. Total Participants Adults (30+ years old)328 Adults (30+ years old) Adolescents (13-19 years old)118 Adolescents.

A presentation of research conducted by Erin L. Percival, with faculty sponsor Dr. Kent Drummond, and support from Wyoming EPSCoR.

WLD: A Robust Local Image Descriptor Jie Chen, Shiguang Shan, Chu He, Guoying Zhao, Matti Pietikäinen, Xilin Chen, Wen Gao 报告人：蒲薇榄.

How do you get here?

Jo˜ao Carreira, Abhishek Kar, Shubham Tulsiani and Jitendra Malik University of California, Berkeley CVPR2015 Virtual View Networks for Object Reconstruction.

Under Guidance of Mr. A. S. Jalal Associate Professor Dept. of Computer Engineering and Applications GLA University, Mathura Presented by Dev Drume Agrawal.

2D-CSF Models of Visual System Development. Model of Adult Spatial Vision.

Mood Detection.

Evaluating Techniques for Image Classification

Fast Preprocessing for Robust Face Sketch Synthesis

Week 6 Cecilia La Place.

Vision Reading Group, 2nd of May 2018 Martin Rünz

Rob Fergus Computer Vision

Outline. A 2010 Mapping of the Constructed Surface Area Density for China Preliminary Results.

Domingo Mery Department of Computer Science

Eigenfaces for recognition (Turk & Pentland)

network of simple neuron-like computing elements

AHED Automatic Human Emotion Detection

Presented by: Chang Jia As for: Pattern Recognition

Pattern Recognition & Machine Learning

Paper Reading Dalong Du April.08, 2011.

AHED Automatic Human Emotion Detection

Outline A. M. Martinez and A. C. Kak, “PCA versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp , 2001.

Data hiding method using image interpolation

Domingo Mery Department of Computer Science

An introduction to Machine Learning (ML)

Presentation transcript:

MACHINE VISION GROUP Face image mapping from NIR to VIS Jie Chen Machine Vision Group

MACHINE VISION GROUP Outline Problem Methods Preliminary results Plans for next period

MACHINE VISION GROUP Face image mapping from NIR to VIS Problem –NIR: Near infrared imaging –VIS: Visual light imaging

MACHINE VISION GROUP Face image mapping from NIR to VIS Problem –NIR: Near infrared imaging –VIS: Visual light imaging

MACHINE VISION GROUP Algorithm: Patches mapping Training Training

MACHINE VISION GROUP Algorithm: Patches mapping Training Mapping φi,jφi,j

MACHINE VISION GROUP Look up the KNN A patch of an input sample in S 3 k-th nearest patch in S 1 Weight of k-th nearest neighbor A patch of an input sample in S 4 Corresponding patch of in S 2

MACHINE VISION GROUP Weight computing

MACHINE VISION GROUP Experiments Setup –both S1 and S2 is composed of 300 samples. 50 subjects, each subject has 6 images but in different expression (anger, disgust, fear, happiness, sadness, and surprise). –w f =64, h f =80, w p =16, h p =16, w o =12, h o =12 –Testing:using leave-one-out and K=15.

MACHINE VISION GROUP Reconstructed images (a) Input images in NIR (b) Reconstructed images in VIS using LBP(8,1) and the PSNR (c) Reconstructed images in VIS using the combined Multi-resolution LBP and their PSNR (d) Ground truth in VIS

MACHINE VISION GROUP Multi-resolution LBP (MLBP) (P=4,R=1) (P=8,R=1) (P=12,R=1.5) (P=16,R=2) (P=24,R=3)

MACHINE VISION GROUP PNSR Pixel wise LBP

MACHINE VISION GROUP Multi-resolution LBP

MACHINE VISION GROUP PSNR on MLBP

MACHINE VISION GROUP Plans for next period Training data: –Use more samples (192*10 from CASIA, a group in Beijing, China) Methods: –Combine the methods proposed in the paper (A. Hertzmann, SIGGRAPH, 2001) for better performance