1. An Adaptive Face-recognition Method Based On Phase Information Presented By:- Suvendu Kumar Dash Department of Electronics and Communication Engineering Vel Tech Dr. RR & Dr. SR Technical University Chennai, TamilNadu, India International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM 2015)

2. Overview Introduction Overview of Algorithm Working Principles Result Reference

3. Introduction The main issue of our project is to solve an effective standard face recognition issue. The proposed method is based on a Vander-Lugt Correlator (VLC) with a new correlation method. This method appears very effective despite possible distortions that may exist between the target face and the reference images due to vertical and horizontal rotations. This filter allows us to obtain a better optimization of the space-bandwidth product available than that of conventional composite filters. The phenomenon of local saturation in the Fourier plane is much more critical than for a segmented filter with a composite filter. Due to this reason, the manufacture of the composite filter is based on the local addition of spectral information arising from different references. In our project, an optimization technique is proposed for fully exploiting the capabilities of VLC filter for our specific application.

4. Overview Of Algorithm Basically, it consists of segmenting the Fourier plane of the filter into several zones and allotting a reference image to each zone according to a chosen criterion. In our research work on multi- correlation filters, we showed that it is possible to design a segmented composite filter, merging at least 12 references. Specially, we used a purely energetic criterion that does not take into account the resemblance between the different references. Validation of such optimization of this criterion is illustrated.

5. Overview Of Algorithm Cont.. We begin by performing the Fourier transform (FT) of each image reference. Then, each spectrum is multiplied by its own pass band function Pk, which is calculated as

6. Overview Of Algorithm Cont.. In Figure, we show the correlation plane when the original image (I 0 ) is placed at the input of the correlator. This plane will be used as the reference plane to check the performance of the correlation decision when the reconstructed image is displayed at the input plane. The second input of the correlator is a reconstructed image from phase information (taking four iterations). It is also interesting to note that we can see the robustness of our algorithm, which can improve the correlation between a reconstructed image and a reference image.

7. Overview Of Algorithm Cont.. Which In fact, the noise in the correlation plane has been reduced and the peak-to-correlation energy (PCE), defined as the energy of the peak correlation normalized to the total energy of the correlation plane.

8. Phase Only Filter The POF filter is an optimized version of the classical matched filter H CMF. This filter has a very sharp peak, is very discriminating, but is too sensitive to specify noise arising.

9. Composite Filter This filter allows one to merge multiple versions of a reference image. This type of filter is interesting to solve the correlation filter sensitivity vis-à-vis the rotation and the scaling operations. This filter can be defined as a linear combination of different reference versions: In the composite filter approach, each point of the Fourier plane is involved in the correlation of a given class according to the intensity of its spectrum at this point. However, this may be inconvenient if several classes use the same point in the Fourier plane.

10. Working Principles In this experiment we are working on the recognition of human faces. First, we are taking some reference images which are used for our testing purposes. Secondly, we are taking some target images. Now, through this experiment we are trying to find an approximate correlation between the reference images and target images. At first, we are taking 10 numbers of datasets each dataset having 10 numbers of the same images of one person, which is shown in the Fig. Now we are going to read these images, then find the Fourier transformation of these images.

11. Working Principles Cont.. After doing the Fourier transformation of these images, we will get spectrum of those images. Then we pass those spectrums through the pass-band filter by multiplying spectrums with their respective pass-band functions. Similarly, we get 10 such functions. After getting these functions we have to merge them to get a single function. Then we pass this function through a phase only filter. After this step, we pass this phase only filter output to segmented composite filter.

12. Working Principles Cont.. Then, finally we get our required output of our reference part. Now on target part, we have to give the images as input. Then we have to find the Fourier transform of images.After this we have to find the phase of the images. Using the phases of an image, we get the inverse Fourier transform of an image. Then we pass the inverse Fourier transformation of an image through a phase only filter using phase only reconstructed iterative algorithm. Now, finally we get our required output of target part which shown in the below. Now in this comparison part we have to examine the relationship between the quality of the reconstructed image and the decision performance of the correlator.

13. Result In this research work, suppose we took an image which is of class 2 in the target part, then we get a value at the output by using the testing part of this project. According to those values we can determine the whether the target part input image is of which class. The Table I. shows the results of this project.

14. Result Cont..

15. Conclusion Our main aim of this project was to find a suitable way to comparison between a reference part and the target part. which was developing a correlator used in the finding of a relationship between the quality of the reconstructed image and performance of the correlator.

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