Surveillance Monitoring Tnis Uiboupin Pejman Rasti (Head of Image - - PowerPoint PPT Presentation

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

• f

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
• f

pictorial information for human interpretation

• 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

Methods domain Fourier Wavelet Frequency Multiple Images Single image Spatial

Image Super Resolution

How Type Set of low res. images Multi-Images Image model/prior Single-Image

Multiple-image super-resolution algorithms

• Receive a couple of low-resolution images
• f 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
• f-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

• n

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