Final Project Face Detection and Recognition

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1 Final Project Face Detection and Recognition Submission Guidelines: 1. Follow the guidelines detailed in the course website and information page.. Submission in pairs is allowed for all students registered to the course. 3. The due date is 7/1/014, at 3:59. In this project you will implement and use a few face detection and recognition techniques. Specifically, you will use the Viola&Jones algorithm, Histogarm of Gradients Orientation, Eigenfaces and Fisherfaces. Face detection finds whether there is a face in the image or not, and where it is, while face recognition finds the identity of a detected face in the image. The Viola&Jones face detection algorithm is a popular, learning-based technique that is used in present-day cameras and devices. The Eigenfaces and Fisherfaces are basic algorithms for face recognition that have a strong mathematical basis. However, newer and better algorithms exist for the task of face recognition. The Histogram of Gradients Orientation is a general approach for feature-based computer vision tasks such as clustering, segmentation and recognition. Here you will use it for face detection. Throughout this project you will use part of the database formerly known as the ORL Database of Faces, and several images that we added to it. You can read about the database here: Important Note 1: This project is the concluding assignment of the course. In this project we expect you to be able to find and learn parts of the material yourselves, to show independence and resourcefulness. Some of the tasks in the project are not very detailed, and we expect you to use the lectures, tutorials, additional material that we will publish and external material to fill in the gaps. Important Note : We are aware that several parts of this project have already-made implementations on the internet, and even Matlab built-in functions. You may NOT use any external packages from the internet that perform parts or sections from this project. Permitted Matlab functions will be listed throughout the project. The function list is not inclusive, however if in doubt please verify with us. 1

2 Part 0: Technical notes and introduction We provide you with the following files: face_train.mat: It includes several face images and their labels. Most of the images are taken from the database, and the last few were added by us. You will use this file for templates, and for creating the recognition framework. face_test.mat: It also includes face images and their labels, in a similar format to the previous file. You will use this file to perform some of the tests of the recognition framework. face_detect.mat: It includes three images of people in a group photo. You will use this file to test your face detection framework. violajones.zip: It includes the code for the Viola&Jones face detection technique. Recognition.zip: It includes all stub files for the face recognition parts. 1. Open face_train.mat in Matlab and examine its structure. Show the first image with its label as the title. What is the resolution of the image? What does it contain?. Open face_detect.mat in Matlab. Note the difference of these images from the ones in the previous file. What is the resolution of these images? What is approximately the resolution of the faces in these images? 3. Extract the ZIP packages and follow the instructions inside. Part 1: Detection using Template Matching In this part you will implement the scaled search window technique from the image pyramid tutorial for detecting faces in images. The purpose of this section is to show the results from a general, naïve algorithm with no assumptions on the structure of faces. 1. Randomly select three labels between 1 and 40 and take the first face image from that label from face_train.mat. Show the images and their labels. Compute the SSD measure values in between these three images and include them in the report. These values should represent good matches of faces.. Use the three images from the previous item as face templates in a scaled search window technique with the SSD measure to detect faces in the images in face_detect.mat. Use a few scales. You may attempt to estimate the scale of the faces in these images, and set the scaling factors appropriately. Design your search window scheme in a way that the images that you scale would only have to be down-sampled. 3. Show the SSD maps in all scales. Indicate the SSD values you got in the true locations of the faces in the images. Do you get local minima in these points? Are the values similar to all faces? Are there other locations in the images where you get the same or similar

3 values? Are the values similar in all the images? Compare the values you got to the SSD values from item 1. Why it is better to down-sample images than to up-sample them? 4. Repeat the previous item, but for each image from face_detect.mat use a face template of a person appearing in that image (check indices greater than 40 in face_train.mat). What are the results now? 5. What are your conclusions on face detection in this technique? What is the quality of the results of detecting faces using a template of a particular person? What are the advantages and disadvantages of this technique? Part : Detection using Histogram of Gradients Orientation In this part you will implement a method for face detection based on a Histogram of Gradients Orientation. This technique is close to the well-known HOG technique (Histograms of Oriented Gradients for Human Detection, by N. Dalal and B. Triggs) but is much less complex. Still, those interested can benefit from reading that paper. Both the model face and the search windows are represented as follows: divide the model face into a D array of non-overlapping cells, and find the weighted histogram of gradient orientation. Normalize the histogram of each cell such that its total sum is 1. The collection of normalized histograms will be the descriptor of the model face or search window. Use the distance for comparing histograms, and the sum of these distances for comparing descriptors. 1. Randomly select a label between 1 and 40 and take the first face image from that label from face_train.mat. Calculate the gradient images of this face image and show the results. What gradient operator did you choose and why?. Divide the image into non-overlapping cells and calculate a gradients orientation histogram of 9 bins for each one, weighted by the gradient magnitude in each pixel. Normalize the histogram of each cell independently. Show a few of these histograms and explain their relation to their corresponding cells (Tip: small cells will generate dull histograms, while large cells aren t local. Choose wisely). 3. First, you will check the quality of the proposed descriptor. Compute a few of such face descriptors of face images from face_train.mat. Always select the first image from a certain label. Measure the distances between these descriptors. What values do you get? These values should represent good matches of faces. 4. Run the scaled search window method from part one, but instead of using face images, you should measure the distance between descriptors. For each search window in the 3

4 image, create a descriptor as described above and check its distance from the face descriptor. Record the scores you get for each window in a D matrix. 5. Show the maps in all scales. Indicate the values you got in the true locations of the faces in the images. Do you get local minima in these points? Are the values similar to all faces? Are there other locations in the images where you get the same or similar values? Are the values similar in all the images? Are any of these values similar to the values obtained in item 3? 6. What are your conclusions on face detection in this technique? What are the advantages and disadvantages of this technique? Compare it to the technique in part This item allows more freedom and lets you use your creativity. Briefly suggest a few ways to improve the descriptor and the comparison method, both in accuracy and speed. You do not need to implement any of your suggestions. A bonus of up to 4 points will be rewarded to an implementation of an improvement and showing its effectiveness. Part 3: Detection using Viola&Jones In this part you will use the Viola&Jones technique for detecting faces in images. This algorithm constructs, using a learning process, a certain model for a face and uses responses from combinations of simple filters applied to the image to detect faces in images. You will need Matlab R011 or later for this part. Items -4 do not depend on item You will learn about the Viola&Jones algorithm in the lecture. Explain the algorithm in a few paragraphs in your own words (answer this item after the relevant lecture).. Use the algorithm on the images in face_detect.mat. Show the results of the detection. Did the algorithm detect all the faces? Where there false detections? 3. Crop and store the faces detected by the algorithm in separate PNG format images. Note: the code may return a detection smaller than the full face. You need to crop a rectangle that includes the chin, the ears or part of them, and a part of the hair. Look in face_train.mat for the expected input and resolutions. You should fit the images in terms of content, size and aspect ratio. 4. Repeat item on scaled-down versions of the images (smaller resolutions). Show a few of the results. At what resolution does the algorithm fail to detect a face that was once detected? What is the approximate resolution of a face in the image at that point? What do you conclude regarding the algorithm ability to detect faces in different scales? 4

5 Part 4: Recognition using Eigenfaces In this part you will implement the Eigenfaces technique from the tutorial for face recognition. You will use it to label faces from the database and also faces from the detection images, and measure the accuracy of the recognition. 1. Explain the Eigenfaces method in a few sentences in your own words.. Complete the stub file face.m that creates a PCA basis for a set of images. Use the documentation in the stub file for your implementation. You may use only the Matlab functions cov and eigs. 3. Run the function using the images in face_train.mat as input with K=100. Save all outputs of this function to e_trained.mat. Submit this file with your work. 4. Complete the stub file ploteigface.m that saves the mean vector and the first K basis components as PNG images in the size of the original train images. 5. Show the first 10 basis components and the mean vector as images. Explain what you see in these images and why. 6. Complete the stub file projectface.m that projects a face image into the PCA space. 7. Complete the stub file reconstructface.m that reconstructs the face image from a feature vector. The feature vector may be of potentially different sizes. 8. Complete the stub file reconstruct_exps.m that tests the Eigenfaces reconstruction framework that you implemented, by taking the first 5 face images from face_test.mat and project each of them into the PCA space with 4 different K values [5, 10, 50, 100]. Then reconstruct all 0 faces (5x4) and save the results as PNG images. Compare the reconstructed faces and the original matching ones both qualitatively and quantitatively (e.g. using MSE). Explain the visual differences between the images, show the images and their corresponding MSE value. 9. Complete the stub file projecttrain.m that computes the PCA projections for all images in face_train.mat. 10. Complete the stub file recognizeface.m that performs face recognition. This function can get a single face image or multiple face images and identify them from the database of people. Use a simple metric for classification. Explain your implementation. 11. Run the function from the previous item with the images in face_test.mat and compute the accuracy of recognition with K=[1, 3, 5, 10, 5, 50, 100]. Plot a graph of accuracy vs. K. Show the graph and explain it. 5

6 1. Run the function with the images you saved in part 3, item 4. Note that you may need to further crop and resize the images to fit them to the resolution of the database. Use the same K values as in the previous item. Were you able to get identification of the images? For each image, write the K value needed to get identification. If no identification could have been made for a certain image, try to explain why. Part 5: Recognition using Fisherfaces In this part you will implement the Fisherfaces technique for face recognition. This technique is similar to the Eigenfaces technique, with a few important differences that improve its recognition performance. Again, you will use it to label faces from the database and also faces from the external images. 1. Read about the Fisherfaces method in the slides we published on the website, and explain the method in a few sentences. What are the main differences from the Eigenfaces method?. Complete the stub file fishertrain.m that computes the Fisher basis for a specific labeling of the training data, by following these steps: a. Use face to compute a basis with K= N c components. This will be W. b. Compute the S B and S W scatter matrices on the training data. c. Multiply S B and S by W W in the following way: S W S W. d. Compute W fld by solving for the eigenvectors of the largest c 1 generalized eigenvalues of Sw B i isw W i (you may use eigs to do that). e. The resulting Fisher basis is W WfldW. 3. Run the function using the images in face_train.mat as input with c=[1, 43, 70]. Use the provided alternative labeling. Save all outputs to f_trained.mat. 4. Repeat items 4-1 from the previous part. You may use the Eigenfaces implementation for the Fisherfaces implementation. When you do not need to change a function, you may use the exact same function. When you do change a function, you must make a copy and rename it for the Fisherfaces framework. 5. Compare the results obtained by the Eigenfaces method to those obtained by the Fisherfaces method both qualitatively and quantitatively. Show graphs and explain them. T 6

7 References [1] N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, Proc. of CVPR, San Diego, California, USA, pp , 005. [] P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proc. of CVPR, Kauai, Hawaii, USA, Vol. I, pp , 001. [3] P.N. Belhumeur, J.P. Hespanha and D.J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE trans. on PAMI, Vol. 19, pp ,

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