Facial Image Super Resolution Using Sparse Representation for Improving Face Recognition in Surveillance Monitoring

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1 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,

2 Outline Problem Introduction to super resolution Introduction to face recognition Proposed method Experimental results Conclusion

3 Face Recognition Face recognition is of great importance in many computer vision applications, such as human-computer interactions, Security systems, Military and Homeland Security.

4 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

5 Problem Essex database Feret database HP database ifr database 99.20% 75.87% 31.6% 20% 50.67% 26.67% 98.06% 76.13%

6 Image up-sampling/enhancement Image Interpolation Super Resolution

7 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

8 Image Super Resolution The desire for high-resolution comes from two principal application areas: Improvement of 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.).

9 Image Super Resolution domain Frequency Spatial Methods Fourier Wavelet Multiple Images Single image

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

11 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.

12 Multiple-image super-resolution algorithms Iterative back projection Iterative adaptive filtering Direct methods Projection onto convex sets Maximum likelihood Maximum a posteriori

13 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.

14 single-image super-resolution algorithms Learning-based single-image SR algorithms Reconstruction-based single-image SR algorithms

15 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.

16 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.

17 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

18 Face recognition pre-processing image enhancement noise removal both of them

19 face detection Viola-Jones Face recognition

20 Face recognition The facial components of region of interest (ROI) mouth eyes ear cheeks nose fore-head eyebrow

21 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)

22 Face recognition classification support vector machine (SVM) artificial neural network (ANN) classifier Hidden Markov Model

23 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.

24 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 highresolution image.

25 Proposed Method

26 Databases

27 Experimental Result The Essex, HP, ferret and ifr database has been employed.

28 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

A Learning Based Method for Super-Resolution of Low Resolution Images

A Learning Based Method for Super-Resolution of Low Resolution Images Emre Ugur June 1, 2004 emre.ugur@ceng.metu.edu.tr Abstract The main objective of this project is the study of a learning based method