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. c++ - Facial Feature Points Detection using OpenCV Whitening - Ufldl From Ufldl Introduction We have used PCA to reduce the dimension of the data. 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 . are uncorrelated, satisfying one of our desiderata for whitened data (that the features be less correlated). To make each of our input features have unit variance, we can simply rescale each feature by . as follows: Plotting , we get: This data now has covariance equal to the identity matrix . is our PCA whitened version of the data: The different components of are uncorrelated and have unit variance. Whitening combined with dimensionality reduction. components of . will be nearly zero anyway, and thus can safely be dropped. ZCA Whitening isn't unique. . When
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. I must admit, I'm quite new to python, and the fact that there isn't much documentation on this particular example, I'm having trouble making it work. Currently if I try running it in its original code (The only thing I changed was making so instead of capturing frames from a video file, it captures from a live camera feed) it simply does not detect any faces.. to it gives me this error:
william_ECCV_FaceRecognition.pdf Eric Yuan's Blog (29) What is the difference between L1 and L2 regularization? 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 firstname.lastname@example.org. 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.
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.
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.
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. I've compiled the code on Ubuntu and it works very well, provided the bounding box you give it is "just right".
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. Code This tutorial code’s is shown lines below. 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?
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). 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!
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