face recognition and its applications based on works of: jinshan tang; ariel p from hebrew...
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Face Recognition and Its applications
Based on works of: Jinshan Tang; Ariel P from Hebrew University; Mircea Focşa, UMFT; Xiaozhen Niu,Department of Computing Science, University of Alberta; Christine Podilchuk, [email protected], http://www.caip.rutgers.edu/wiselab
PART 1
ContentsIntroductionFace detection using color informationFace matchingFace Segmentation/DetectionFacial Feature extractionFace RecognitionVideo-based Face RecognitionComparisonConclusionReference
Face Segmentation/Detection
During the past ten years, considerable progress has been made in multi-face recognition area, This includes: Example-based learning approach by Sung and
Poggio (1994). The neural network approach by Rowley et al. (1998). Support vector machine (SVM) by Osuna et al. (1997).
Input face image
Face detection
Face feature extraction
Feature Matching Decision maker
Output result
Basic steps for face recognition
Face database
Face recognition
Face detection• Geometric information based face detection
• Color information based face detection
• Combining them together
(a) Geometric information based face detection
(b) Color information based face detection
Color information based face detection
Face color is different from backgroundChoice of color spaces is very importantColor Spaces:
•R,G,B
•YCbCr
•YUV
•r,g
•……..
Skin color
Background color
Figure 4. Skin color distribution in a complex background
Ideas: (1) compensate for lightning, (2) separate by transforming to new (sub) space.
(3) clustering.
Location and shape parameters of eyes are the most important features to be detected through segmentation and morphological operations (dilation and erosion).
Ideas:
1) Eyes
2) Mouth
3) Boundary (edge detection)
4) Boundary approximated to ellipse or something (Hough)
•Feature based face matching
Face image From face detection
Normalization Feature extraction
Feature vector
classifierDecision maker
Output results
You can extract various features
You can use various classifiers
You can use various decision makers
Normalization
)()(
))(()()y(
TI
TImeanTImeanC
T
TN
Eye location Normalization: rotation
normalization, scale normalization
Cross Correlation :
object template
Averaged for objects
Feature extraction•Eyebrow thickness and vertical position at the eye center position
•A coarse description of the left eyebrow’s arches
•Nose vertical position and width
•Mouth vertical position, width, height upper and lower lips
• eleven radii describing the chin shape
•Bigonial breadth (face width at nose position)
•Zygomatic breadth (face width halfway between nose tip and eyes).
3.5-D feature vector
Classifier
1)()()( j
Tjj mxmxx
Bayes classifier
Feature vector
Computer )(xj
x
(j=2,3,…N)j
m
Rank the distance values
)(xj
Output the results
This is just one example of classifier, others are Decision Trees, expressions, decomposed structures, NNs.
ANN Classifier
ANN
one-class-in-one network
multi-class-in-one network
Feature vector
Class 1 Class 2
MAXNET
Classification results
Fig.2. one-class-in-one network
Template matching Produce a template
Face image From face detection
Normalization
Decision maker
Output results
matching
Templatesdatabase
You have to create the data base of templates for all people you want to recognize
There are different templates used in various regions of the normalized face.
Various methods can be used to compress information for each template.
Example-based learning approach (EBL)
Three parts:The image is divided into many possible-overlapping windows, each window pattern gets classified as either “a
face” or “not a face” based on a set of local image measurements.
For each new pattern to be classified, the system computes a set of different measurements between the new pattern and the canonical face model.A trained classifier identifies the new pattern as “a face” or “not a face”.
Neural network (NN)
Kanade et al. first proposed an NN-based approach in 1996.Although NN have received significant attention in many research areas, few applications were successful in face recognition.
Why?
Neural network (NN)
It’s easy to train a neural network with samples which contain faces, but it is much harder to train a neural network with samples which do not.The number of “non-face” samples are just too large.
Neural network (NN)
Neural network-based filter. A small filter window is used to scan through
all portions of the image, and to detect whether a face exists in each
window.
Merging overlapping detections and arbitration. By setting a small threshold, many false detections can be eliminated.
SVM (Support Vector Machine)
SVM was first proposed in 1997, it can be viewed as a way to train polynomial neural network or radial basic function classifiers.Can improve the accuracy and reduce the computation.
Comparison with Example Based Learning (EBL)
Test results reported in 1997.Using two test sets (155 faces). SVM achieved better detection rate and
fewer false alarms.
Recent approaches
Face segmentation/detection research area still remain active, for example: An integrated SVM approach to multi-face
detection and recognition was proposed in 2000.
A technique of background learning was proposed in August 2002.
Still lots of potential!
Static face recognition
Numerous face recognition methods/algorithms have been proposed in last 20 years, several representative approaches are:
Eigenface LDA/FDA (Linear DA, Fisher DA) Discriminant analysis
(algorithm) Neural network (NN) PCA – Principal Component Analysis Discrete Hidden Markov Models (DHMM) Continuous Density HMM (CDHMM).
EigenfaceThe basic steps are:
Registration. A face in an input image first must be located and registered in a standard-size frame.Eigenpresentation. Every face in the database can be represented
as a vector of weights, the principal component analysis (PCA) is
used to encode face images and capture face features.
Identification. This part is done by locating the images in the database whose weights are the closest (in Euclidean distance) to the weights of the test images.
LDA/FDAFace recognition method using LDA/FDA is called the fishface method.Eigenface use linear PCA. It is not optimal to discrimination for one face class from others.Fishface method seeks to find a linear transformation to maximize the between-class scatter and minimize the within-class scatter.Test results demonstrated LDA/FDA is better than eigenface using linear PCA (1997).
Video-based Face Recognition
Three challenges: Low quality Small images Characteristics of face/human objects.
Three advantages: Allows much more information. Tracking of face image. Provides continuity,
this allows reuse of classification information from high-quality images in processing low-quality images from a video sequence.
Basic steps for video-based face recognition
Object segmentation/detection.Motion structure. The goal of this step is to estimate the 3D
depths of points from the image sequence.
3D models for faces. Using a 3D model to match frontal views of
the face.
Non-rigid motion analysis.
Recent approachesMost video-based face recognition system has three modules for
detection, tracking and recognition.
An access control system using Radial Basis Function (RBS) network was proposed in 1997.
A generic approach based on posterior estimation using sequential Monte Carlo methods was proposed in 2000.
A scheme based on streaming face recognition (SFR) was propose in August 2002.
The Streaming Face Recognition (SFR) schemeCombine several decision rules together, such as: Discrete Hidden Markov Models (DHMM) and Continuous Density HMM (CDHMM).
The test result achieved a 99% correct recognition rate in the intelligent room.
ComparisonTwo most representative and important protocols for face recognition evaluations: The FERET protocol (1994).
Consists of 14,126 images of 1199 individuals. Three evaluation tests had been administered in
1994, 1996, and 1997. The XM2VTS protocol (1999).
Expansion of previous M2VTS program (5 shots of each of 37 subjects).
Now consists 295 subjects. The results of M2VTS/XM2VTS can be used in wide
range of applications.
• Face recognition has many potential applications.• For many years not very successful,
• we need to improve the accuracy of face recognition
• Combining face recognition and other biometric recognition technologies,
•Such as:• fingerprint recognition technology, • voice recognition technologies • and so on
Conclusion
For our applications accuracy is much more important than speed.
Significant achievements have been made recently. LDA-based methods and NN-based methods are
very successful.
FERET and XM2VTS have had a significant impact to the developing of face recognition algorithms.Challenges still exist, such as pose changing and illumination changing. Face recognition area will remain active for a long time.
Conclusion
Reference[1] W. Zhao, R. Chellappa, A. Rosenfeld, and P.J. Phillips, Face Recognition: A Literature Survey, UMD CFAR Technical Report CAR-TR-948, 2000.[2] K. Sung and T. Poggio, Example-based Learning for View-based Human Face Detection, A.I. Memo 1521, MIT A.I. Laboratory, 1994.[3] H.A. Rowley, S. Baluja, and T. Kanade, Neural Network Based Face Detection, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 20, 1998.[4] E. Osuna, R. Freund, and F. Girosi, Training Support Vector Machines: An Application to Face Recognition, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 130-136, 1997.[5] M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, Vol.3, pp. 72-86, 1991.
[6] W. Zhao, Robust Image Based 3D Face Recognition, PhD thesis, University of Maryland, 1999.[7] K.S. Huang and M.M. Trivedi, Streaming Face Recognition using Multicamera Video Arrays, 16th International Conference on Pattern Recognition (ICPR). August 11-15, 2002.[8] P.J. Phillips, P. Rauss, and S. Der, FERET (Face Recognition Technology) Recognition Algorithm Development and Test Report, Technical Report ARL-TR 995, U.S. Army Research Laboratory.[9] K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, XM2VTSDB: The Extended M2VTS Database, in Proceedings, International Conference on Audio and Video-based Person Authentication, pp. 72-77, 1999.