Facial Image Super Resolution Using Sparse …cs.ioc.ee/~tarmo/tday-kao/rasti-slides.pdf•Image...
Transcript of Facial Image Super Resolution Using Sparse …cs.ioc.ee/~tarmo/tday-kao/rasti-slides.pdf•Image...
Facial Image Super Resolution
Using Sparse Representation for
Improving Face Recognition in
Surveillance Monitoring
Tõnis Uiboupin
Pejman Rasti
(Head of Image Processing division of iCV Group)
Gholamreza Anbarjafari,
Outline
•Problem
•Introduction to super resolution
•Introduction to face recognition
•Proposed method
•Experimental results
•Conclusion
Face Recognition
•Face recognition is of great importance in
many computer vision applications, such
as human-computer interactions, Security
systems, Military and Homeland Security.
Problem
•face recognition systems mostly work with
images\videos of proper quality and
resolution.
•In videos recorded by surveillance
camera, due to the distance between
people and cameras, people are pictured
very small and hence challenge face
recognition algorithms
Problem
Essex database Feret database HP database iFR database
99.20% 75.87% 31.6% 20% 50.67% 26.67% 98.06% 76.13%
Image up-sampling/enhancement
•Image Interpolation
•Super Resolution
image Interpolation
•Image interpolation is one of the basic methods for up-sampling images
•Some of the famous interpolation techniques are:–Nearest neighbor
–Bilinear
–Bicubic
•The high frequency details are not restored
Image Super Resolution
•The desire for high-resolution comes from two principal application areas:
• Improvement of pictorial information for humaninterpretation
• Helping representation for automatic machine perception
•The application of SR techniques covers a wide range of purposes such as Surveillance video, Remote sensing, Medical imaging (CT, MRI, Ultrasound.).
Image Super Resolution
Methodsdomain
Fourier
Wavelet
Frequency
Multiple Images
Single image
Spatial
Image Super Resolution
HowType
Set of low res. imagesMulti-Images
Image model/priorSingle-Image
Multiple-image super-resolution
algorithms
•Receive a couple of low-resolution images
of the same scene as input and usually
employee a registration algorithm to find
the transformation between them.
Multiple-image super-resolution
algorithms
•Iterative back projection
•Iterative adaptive filtering
•Direct methods
•Projection onto convex sets
•Maximum likelihood
•Maximum a posteriori
single-image super-resolution
algorithms•During the sub-sampling or decimation of an image, the desired high-frequency information gets lost. Multiple super resolution methods cannot help recover the lost frequencies, especially for high improvement factors
•Single-image super-resolution algorithms do not have the possibility of utilizing sub-pixel displacements, because they only have a single input.
single-image super-resolution
algorithms
•Learning-based single-image SR
algorithms
•Reconstruction-based single-image SR
algorithms
single-image super-resolution
algorithms
•Learning-based single-image SR algorithms
–These algorithms, as learning-based or
Hallucination algorithms were first introduced in
which a neural network was used to improve the
resolution of fingerprint images.
–These algorithms contain a training step in which
the relationship between some HR examples
(from a specific class like face images,
fingerprints, etc.) and their LR counterparts is
learned.
single-image super-resolution
algorithms
•Reconstruction-based single-image SR
algorithms
–These algorithms similar to their peer multiple
image based SR algorithms try to address the
aliasing artifacts that are present in the LR
input image.
Face recognition
•In general, face recognition consist of 5
steps
–pre-processing
–face detection
–The facial components of region of interest
(ROI)
–feature extraction
–classification
Face recognition
•pre-processing
–image enhancement
–noise removal
–both of them
Face recognition
• face detection
–Viola-Jones
Face recognition
• The facial components of region of interest (ROI)– mouth
– eyes
– ear
– cheeks
– nose
– fore-head
– eyebrow
Face recognition
•feature extraction
–Local Binary Patterns (LBP)
– Gabor filters
–Linear Discriminant Analysis (LDA)
–Principal Component Analysis (PCA)
–Local Gradient Code (LGC)
–Independent Component Analysis (ICA)
Face recognition
• classification
–support vector machine (SVM)
–artificial neural network (ANN) classifier
–Hidden Markov Model
solution
•we investigate the importance of the state
of-the-art super-resolution algorithm in
improving recognition accuracies of the
state-of-the-art face recognition algorithm
for working with low-resolution images.
Proposed Method
•Having a low-resolution input images, the
proposed system upsamples it by the
sparse representation super-resolution
algorithm. Then, the SVD and Hidden
Markov Model algorithm are used to
perform face recognition on the high-
resolution image.
Proposed Method
Databases
Experimental Result
•The Essex, HP, ferret and ifr database has
been employed.
Conclusion
•State-of-the-art face recognition algorithms, like Hidden
Markov Model and SVD have difficulties handling
videos\images that are of low quality and resolution.
•we have proposed to use upsampling techniques.
•Experimental results on a down-sampled version of the
benchmark databases show that the proposed is efficient
in improving the quality of such lowresolution images and
hence improves the recognition accuracy of the face
recognition algorithm