background preloader

DBN_face_recog

Facebook Twitter

PII: S0262-8856(02)00136-1 - TCASM.pdf. Bioidiap/bob.ip.flandmark. Software. Documentation | Face Analysis SDK. This section outlines the non-rigid face registration program face-fit. This program can perform fitting on a single image, a sequence of images or video. An important detail of the fitting algorithm is that it relies on a frontal face detector to initialize the non-rigid fitting component. Once initialized, it falls back to the frontal face detector only when the fitting algorithm has failed to accurately perform non-rigid registration.

The following command executes the fitting algorithm on a single image and visualises the results. face-fit <image> The resulting landmarks can be saved to file by specifying an output pathname as a command line argument. face-fit <image><output-landmarks> The next command performs tracking over a sequence of images. face-fit --lists <image-lists> [landmarks-list] The argument <image-lists> is a file containing a list of image pathnames with each pathname separated by a new line. The --video switch enables face-fit to perform fitting on a video. Flandmark - open-source implementation of facial landmark detector. News 11-11-2012 - New version of flandmark with better internal structure and improved MATLAB interface available! Introduction flandmark is an open source C library (with interface to MATLAB) implementing a facial landmark detector in static images. Detector parameters learning is written solely in MATLAB and is also part of flandmark.

The input of flandmark is an image of a face. Flandmark (version 1.06) can be also used in python thanks to the following project: xbob.flandmark 1.0.2. Sample results The landmark detector processes each frame separately, i.e. temporal continuity of landmark positions is not exploited. CNN anchorwoman. Resolution: 640x360, Frames: 1383, Size: 5MB Video captured by the head camera of humanoid robot NAO. Resolution: 320x240, Frames: 669, Size: 13MB Movie "In Bruges". Resolution: 720x304, Frames: 300, Size: 2.9M Structured output classifier flandmark uses a structured output classifier based on the Deformable Part Models (DPM). Graph constraints Components Download. Asmlib-opencv - an ASM(Active Shape Model) implementation by C++ using opencv 2. An open source Active Shape Model library written by C++ using OpenCV 2.0 (or above), no other dependencies. Thanks to CMake, the library has been successfully compiled in following environments: Linux (both 32 and 64 bits) Windows(both VC and MinGW) Mac OS X Android Both training and fitting codes are provided.

For Windows users, a binary demo is available for download. The library implements ASM and BTSM(Bayesian Tangent Shape Model). I think its result is good for most frontal faces. The fitting speed is fast, on my laptop computer(Intel(R) Core(TM)2 Duo CPU P8600 @ 2.40GHz), it can do real-time fitting on a webcam (see RunningDemo). With the code provided, you can also train your own model all by yourself (see BuildModel). Codes are in the SVN repository, click "Source" to check them out. If you have questions, please either file a issue or contact me directly (cxcxcxcx, gmail). If you use the library in your project, please just add a link to this project page, thanks! Cascade Classifier. Goal In this tutorial you will learn how to: Use the CascadeClassifier class to detect objects in a video stream. Particularly, we will use the functions:load to load a .xml classifier file.

It can be either a Haar or a LBP classiferdetectMultiScale to perform the detection. Code This tutorial code’s is shown lines below. You can also download it from here . Result Here is the result of running the code above and using as input the video stream of a build-in webcam: Remember to copy the files haarcascade_frontalface_alt.xml and haarcascade_eye_tree_eyeglasses.xml in your current directory. Help and Feedback You did not find what you were looking for? Facial feature detection. Answered Oct 3 '12 lightalchemist131 ● 2 ● 4 The Flandmark Facial point detector (with code) can be found here: It will return you the four corner points of the eyes, corner of mouth, center of nose, and center of face. It does however require you to give it a bounding box of the face so you will probably have to use the Viola Jones face detector in OpenCV (or any other method) to locate the face first, which you are already doing.

I've compiled the code on Ubuntu and it works very well, provided the bounding box you give it is "just right". If it is too tightly cropped it might miss the feature points near the border of the image. For such cases, you can try to extend the border and specify the bounding box as the "inner" image (excluding border) and sometimes it works. I·bug - resources - Facial point detector (2010/2013) LEAR: Local Evidence Aggregation for Regression Based Facial Point Detection The LEAR programme detects 20 fiducial facial points.

Instead of scanning an image or image region for the location of a facial point, it can use every location in a point's neighbourhood to predict where the target point is relative to that location. This considerably speeds up point detection. In order to obtain a precise prediction of the target location, a stochastic process decides the local patches used to sample from. More information about the point detector can be found in: B. We kindly request you to cite this work if you decide to use the point detector for research purposes. Please note that we have re-annotated the BioID images as our point criterion, for which we already had abundant annotations, was different for some of the points. The detector can be downloaded from the following links: BoRMaN - Boosted Regression with Markov Networks for Facial Point Detection Michel F. A comparative study on illumination preprocessing in face recognition. A Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, Chinab Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USAc School of Electrical Engineering and Computer Science, Peking University, Beijing 100871, China Received 22 February 2012, Revised 6 November 2012, Accepted 21 November 2012, Available online 1 December 2012 Choose an option to locate/access this article: Check if you have access through your login credentials or your institution Check access DOI: 10.1016/j.patcog.2012.11.022 Get rights and content Abstract Illumination preprocessing is an effective and efficient approach in handling lighting variations for face recognition.

Highlights Keywords Face recognition; Illumination-insensitive; Illumination preprocessing; Comparative study; Holistic approach; Localized approach; Band integration Copyright © 2012 Elsevier Ltd. Welcome — Pylearn2 dev documentation. Pylearn2 is still undergoing rapid development. Don’t expect a clean road without bumps! If you find a bug please write to pylearn-dev@googlegroups.com. If you’re a Pylearn2 developer and you find a bug, please write a unit test for it so the bug doesn’t come back!

Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. Researchers add features as they need them. There is no PyPI download yet, so Pylearn2 cannot be installed using e.g. pip. Git clone To make Pylearn2 available in your Python installation, run the following command in the top-level pylearn2 directory (which should have been created by the previous command): You may need to use sudo to invoke this command with administrator privileges.

Python setup.py develop --user This command will also compile the Cython extensions required for e.g. pylearn2.train_extensions.window_flip. Data path export PYLEARN2_DATA_PATH=/data/lisa/data Ian J. (29) What is the difference between L1 and L2 regularization? Datasets — Pylearn2 dev documentation. Eric Yuan's Blog. Active Shape Model (ASM) Videofacerec.py example help. Asked Jul 12 '12 coredumped3 ● 1 ● 2 Hello, I'm currently doing some work in face recognition with small training data samples (typically only one per person).

I was initially trying to do face recognition with eigenfaces but getting terrible results, and was guided to use Local Binary Patterns Histograms by this stack overflow post: I started to study the libfacerec as suggested and tried to use the facerec_lbph.cpp example which uses the Local Binary Patterns Histograms to recognize faces. However, my results were still very poor. I suspect the reason for this is that my preprocessing method for the images was applying grayscale and histogram equalization. The stack overflow post linked above suggested using the TanTriggs method to preprocess the images in order to achieve good results. Img = cv2.resize(frame, (frame.shape[1]/2, frame.shape[0]/2), interpolation = cv2.INTER_CUBIC) to. Whitening - Ufldl. From Ufldl Introduction We have used PCA to reduce the dimension of the data.

There is a closely related preprocessing step called whitening (or, in some other literatures, sphering) which is needed for some algorithms. If we are training on images, the raw input is redundant, since adjacent pixel values are highly correlated. The goal of whitening is to make the input less redundant; more formally, our desiderata are that our learning algorithms sees a training input where (i) the features are less correlated with each other, and (ii) the features all have the same variance. 2D example We will first describe whitening using our previous 2D example.

How can we make our input features uncorrelated with each other? . Was: The covariance matrix of this data is given by: (Note: Technically, many of the statements in this section about the "covariance" will be true only if the data has zero mean. It is no accident that the diagonal values are and . By . As follows: Plotting , we get: . Components of .

C++ - Facial Feature Points Detection using OpenCV. Face Recognition | Visual Information Processing and Learning. Research topic #5: Lighting Preprocessing Lighting preprocessing aims at achieving illumination-insensitive face recognition by removing uneven illumination (e.g. shadow, underexposure, and overexposure) that appears in face images. Our work on lighting preprocessing includes four main approaches: 1) Image enhancement based methods. Illumination in face images leads to skewed intensity distribution. Therefore, uneven lighting in face images can be normalized by correcting the skewed intensity distribution. Selected Publications: [Shan AMFG’03] S. [PDF](1.44 MB)[Qing IJPRAI’05] L. [PDF](384.18 KB)[Han PR’13] H. [PDF](795.17 KB)[Han ECCV’12] H. UFLDL Tutorial - Ufldl. Matlab codes for dimensionality reduction. Color constancy in different illumination condition. William_ECCV_FaceRecognition.pdf.

The INFace Toolbox. The INFace toolbox v2.1 includes (among others) implementations of the following photometric normalization techniques*: - the single-scale-retinex algorithm - the multi-scale-retinex algorithm - the single-scale self quotient image - the multi-scale self quotient image - the homomorphic-filtering-based normalization technique - a wavelet-based normalization technique - a wevelet-denoising-based normalization technique - the isotropic-diffusion-based normalization technique - the anisotropic-diffusion-based normalization technique - the non-local-means-based normalization technique - the adaptive non-local-means-based normalization technique - the DCT-based normalization technique - a normalization technique based on steerable filters - the Gradientfaces-based normalization technique - a modified anisotropic smoothing normalization technique - the Weberfaces-based normalization technique - the multi-scale Weberfaces normalization technique - the large and small scale features normalization technique.

Installation Instructions — The Face Recognition Library (FaceRecLib) 2.0.0b1 documentation. Download FaceRecLib To have a stable version of the FaceRecLib, the safest option is to go to the FaceRecLibs web page on PyPI and download the latest version. Nevertheless, the library is also available as a project of Idiap at GitHub. To check out the current version of the FaceRecLib, go to the console, move to any place you like and call: $ git clone git@github.com:idiap/facereclib.git Be aware that you will get the latest changes and that it might not work as expected. Bob The FaceRecLib is a satellite package of Bob, where most of the image processing, feature extraction, and face recognition algorithms, as well as the evaluation techniques are implemented.

To install Packages of Bob, please read the Installation Instructions. Note Currently, running Bob under MS Windows in not yet supported. Usually, all possible database satellite packages (called bob.db.[...]) are automatically downloaded from PyPI. The CSU Face Recognition Resources $ bin/buildout -c buildout-with-csu.cfg Warning. Tan-amfg07a.pdf. Illumination issue of face detection in OpenCV 2.x (or above) Facerec/py/facerec at master · bytefish/facerec. Facereclib.preprocessing.TanTriggs. Is not available. ECCV 2012 Short Course Sparse Representation and Low-Rank Representation in Computer Vision -- Theory, Algorithms, and Applications Speakers: Yi Ma – Microsoft Research Asia John Wright – Columbia University, New York Allen Y.

Yang – University of California, Berkeley Date: TBD Duration: Half-Day Description: The recent vibrant study of sparse representation and compressive sensing has led to numerous groundbreaking results in signal processing and machine learning. Online Source Code and References: Session 1: Introduction to Sparse Representation and Low-Rank Representation. This session introduces the basic concepts of sparse representation and low-rank representation. Session 2: Variations of Sparse Optimization and Their Numerical Implementation. This session discusses several extensions of the basic sparse representation concept, from the original l-1 minimization formulation to group sparsity, Sparse PCA, Robust PCA, and compressive phase retrieval. Speaker Bios: Allen Y. Is not available. Google's Secretive DeepMind Start-up Unveils A "Neural Turing Machine" One of the great challenges of neuroscience is to understand the short-term working memory in the human brain.

At the same time, computer scientists would dearly love to reproduce the same kind of memory in silico. Today, Google’s secretive DeepMind startup, which it bought for $400 million earlier this year, unveils a prototype computer that attempts to mimic some of the properties of the human brain’s short-term working memory. The new computer is a type of neural network that has been adapted to work with an external memory. The result is a computer that learns as it stores memories and can later retrieve them to perform logical tasks beyond those it has been trained to do. DeepMind’s breakthrough follows a long history of work on short-term memory. In the 1950s, the American cognitive psychologist George Miller carried out one of the more famous experiments in the history of brain science. That raises the curious question: what is a chunk? Here is an example. Deep Learning, NLP, and Representations - colah's blog.

LUO, Ping (羅平) Cvpr12_facealignment.pdf. Aligning face images. C++ Library: Real-Time Face Pose Estimation. Iccv07alignment.pdf. Source Codes | Visual Information Processing and Learning. Face Alignment. RGB Normalization - I'maKash. Histograms - 2: Histogram Equalization. Simple illumination correction in images openCV c++ A comparative study on illumination preprocessing in face recognition. Yi_Towards_Pose_Robust_2013_CVPR_paper.pdf.

Cvpr12.pdf. CLM-WILD Code - Akshay Asthana. Facial Feature Detection | Matthias Dantone. Flandmark - open-source implementation of facial landmark detector. / - asmlib-opencv - an ASM(Active Shape Model) implementation by C++ using opencv 2. I·bug - resources - Facial point detector (2010/2013) ValstarMartinezPantic_final.pdf. Convolutional Neural Networks - Andrew Gibiansky. Neural networks [9.3] : Computer vision - parameter sharing. [1409.6448] HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection. DeepLearning.University – An Annotated Deep Learning Bibliography | Memkite. SunWTcvpr13.pdf. Deep-learning-faces - C++, CUDA, and Matlab implementation of convolutional and fully connected neural nets. Research Detailed References. Untitled - HuangCVPR12.pdf.

Conferences : 2014 : Program : Deep Learning Multi-View Representation for Face Recognition. Xplore Full-Text PDF: Deepface.pdf. Deep Learning in Object Detection and Recognition - overview.pdf. Facebook shows off its deep learning skills with DeepFace. 1404.3606v2.pdf. Deep learning at the University of Chicago.

Data transformation - Is whitening always good? Home Page of Geoffrey Hinton. Multimedia Laboratory. How Facebook's Machines Got So Good At Recognizing Your Face. Face Recognition with OpenCV. FaceRecognizer. Facerec_python.pdf. FisherFaceRecognizer in Python, segment fault. Fisherfaces. Bytefish/facerec. Facial Recognition in Python | Scott Lobdell. Fisherfaces. Challenges in Representation Learning: Facial Expression Recognition Challenge. Face++: Leading Face Recognition on Cloud.

Deep-learning-faces - C++, CUDA, and Matlab implementation of convolutional and fully connected neural nets. Learning-Deep-Face-Representation.pdf. Features for face recognition. Face_rec12.pdf. Dlibrary/JIPS_v05_no2_paper1.pdf. Research.microsoft.com/pubs/132810/PAMI-Face.pdf. List of computer vision topics. Facial recognition system. Feature Selection in Face Recognition: A Sparse Representation Perspective. Cacm2011-researchHighlights-convDBN.pdf. Block-based Deep Belief Networks for face recognition.

Face Detection Matlab Code. Www.ics.uci.edu/~xzhu/paper/face-cvpr12.pdf. Du_diss.pdf. Xplore Full-Text PDF: IEEE.TNN.face.recognition.hybrid.nn.pdf. Icml09-ConvolutionalDeepBeliefNetworks.pdf. Deep Learning Bibliography | Memkite. Deep neural network face recognition.