Face identification
- 1. FACE IDENTIFICATIONFACE IDENTIFICATION SUBMITTED BY:SUBMITTED BY: VIPIN GOYALVIPIN GOYAL SUPERVISED BY:SUPERVISED BY: MR.SANTOSH KUMAR VERMAMR.SANTOSH KUMAR VERMA
- 2. ABSTRACTABSTRACT Face Identification is an application that is mainly used to identify criminals based on the clues given by the eyewitnesses. Based on the clues we develop an image by using the image that we have in our database and then we compare it with the images already we have. To identify any criminals we must have a record that generally contains name, age, location, previous crime, gender, photo, etc. The primary task at hand is, given still or video images require the identification of the one or more segmented and extracted from the scene, where upon it can be identified and matched
- 3. CONTENTSCONTENTS Existing SystemExisting System Proposed SystemProposed System Architecture of the systemArchitecture of the system Algorithms proposedAlgorithms proposed Modules of the systemModules of the system UML and system designUML and system design
- 4. EXISTING SYSTEMEXISTING SYSTEM The development of face identification has been past from the year to years. In recent years to identify any criminal face they used to make a sketch or draw a image based on the eyewitnesses. It used to take more amount of time and it was very difficult task for any investigation department to easily catch the criminals within a stipulated time. In order to catch the criminals first they used to search their record whether to find out is there any record about that particular person in the past. In olden days each and every record was maintained in the books or registers or files which used to contain information about previous criminals with their names, alias name, gender, age, crime involved, etc.
- 5. PROPOSED SYSTEMPROPOSED SYSTEM To overcome the drawbacks that were in theTo overcome the drawbacks that were in the existing system we develop a system that will beexisting system we develop a system that will be very useful for any investigation department.very useful for any investigation department. Here the program keeps track of the recordHere the program keeps track of the record number of each slice during the construction ofnumber of each slice during the construction of identifiable human face and calculate maximumidentifiable human face and calculate maximum number of slices of the similar record number.number of slices of the similar record number. Based on this record number the programBased on this record number the program retrieves the personal record of the suspectretrieves the personal record of the suspect (whose slice constituted the major parts of the(whose slice constituted the major parts of the constructed human face) on exercising theconstructed human face) on exercising the “locate” option.“locate” option.
- 6. ALGORITHMS PROPOSEDALGORITHMS PROPOSED 1.1. Elastic Bunch AlgorithmElastic Bunch Algorithm – This algorithm has– This algorithm has basically three phases as follows:-basically three phases as follows:- Firstly, we use the phase of theFirstly, we use the phase of the complex Gabor waveletcomplex Gabor wavelet coefficientscoefficients to achieve a more accurate location of theto achieve a more accurate location of the nodes and to disambiguate patterns which would be similarnodes and to disambiguate patterns which would be similar in their coefficient magnitudes.in their coefficient magnitudes. Secondly, we employSecondly, we employ object adapted graphsobject adapted graphs, so that, so that nodes refer to specific facial landmarks, callednodes refer to specific facial landmarks, called fiducialfiducial pointspoints. The correct correspondences between two faces. The correct correspondences between two faces can then be found across large viewpoint changes.can then be found across large viewpoint changes. Thirdly, we have introduced a new data structure, calledThirdly, we have introduced a new data structure, called thethe bunch graphbunch graph, which serves as a, which serves as a generalizedgeneralized representation of facesrepresentation of faces by combining jets of a small setby combining jets of a small set of individual faces. This allows the system to find theof individual faces. This allows the system to find the fiducial points in one matching process, which eliminatesfiducial points in one matching process, which eliminates the need for matching each model graph individually. Thisthe need for matching each model graph individually. This reduces computational effort significantly.reduces computational effort significantly.
- 7. Eigenvalues and eigenvectorsEigenvalues and eigenvectors This algorithm is used for identifying theThis algorithm is used for identifying the correct criminal from the store databasecorrect criminal from the store database by matching the constructed image.by matching the constructed image. This algorithm doesn’t works well in lightThis algorithm doesn’t works well in light variations.variations. It assume image as a 1D column vectorIt assume image as a 1D column vector with concatenated rows of pixels or 2Dwith concatenated rows of pixels or 2D array of pixels.array of pixels. Then it compares the pixels of aThen it compares the pixels of a constructed image with that of prestoredconstructed image with that of prestored images and check out the euclideanimages and check out the euclidean distance to find the best possible suspect.distance to find the best possible suspect.
- 8. Pseudocode of the algorithmPseudocode of the algorithm 1. Set image resolution parameter 4 (imres)1. Set image resolution parameter 4 (imres) 2. Set PCA dimensionality parameter (PCADIM)2. Set PCA dimensionality parameter (PCADIM) 3. Read training images3. Read training images 4. Form training data matrix (Mtraindata)4. Form training data matrix (Mtraindata) 5. Form training class labels matrix (Mtrainlabels)5. Form training class labels matrix (Mtrainlabels) 6. Calculate PCA transformation matrix (tmatrix)6. Calculate PCA transformation matrix (tmatrix) 7. Calculate feature vectors of all training images7. Calculate feature vectors of all training images using tmatrixusing tmatrix 8. Store training feature vectors in a matrix8. Store training feature vectors in a matrix 9. Read test faces9. Read test faces 10. For each test face do10. For each test face do
- 9. 11. Calculate the feature vector of a test face using t11. Calculate the feature vector of a test face using t matrixmatrix 12. Compute the distances between test feature vector and12. Compute the distances between test feature vector and all training vectorsall training vectors 13. Store the distances together with the training class13. Store the distances together with the training class labelslabels 14. Initialize error count to zero.14. Initialize error count to zero. 15. For each test face do15. For each test face do 16. Using the distance data, determine the person ID of the16. Using the distance data, determine the person ID of the most similar training vectormost similar training vector 17. If the found ID is not equal to the ID of the test image17. If the found ID is not equal to the ID of the test image increment error countincrement error count 18. Output the correct recognition accuracy :18. Output the correct recognition accuracy : (1 - (error count/ total test image count))*100(1 - (error count/ total test image count))*100
- 10. Modules of the systemModules of the system There are basically four modules of theThere are basically four modules of the system as follows:-system as follows:- Add ImageAdd Image - Add Image is a module that- Add Image is a module that is considered with adding image alongis considered with adding image along with the complete details of the person ofwith the complete details of the person of whom we are taking image.whom we are taking image. Clip Image -Clip Image - This modules main functionThis modules main function is to divide the images into differentis to divide the images into different pieces such as hairs, forehead, eyes, nosepieces such as hairs, forehead, eyes, nose and lips and store them in the databaseand lips and store them in the database and also creates the files onto our system.and also creates the files onto our system.
- 11. Construct Image -Construct Image - Based on theBased on the eyewitnesses we are going to constructeyewitnesses we are going to construct the images. The witness will give usthe images. The witness will give us instruction by looking onto the screen oninstruction by looking onto the screen on which there will be the parts of the imageswhich there will be the parts of the images like eyes, hairs etc.like eyes, hairs etc. Identification -Identification - This module contains theThis module contains the interface to take the image from aboveinterface to take the image from above module and it compares or searches withmodule and it compares or searches with the images already there in the database.the images already there in the database.
- 12. Use Case DiagramUse Case Diagram
- 14. Functional and Non FunctionalFunctional and Non Functional RequirementsRequirements Functional RequirementsFunctional Requirements:-:- The eyewitness should be able to identifyThe eyewitness should be able to identify the correct image properly. There shouldthe correct image properly. There should not be chaos for culprit’s image identity innot be chaos for culprit’s image identity in eyewitness mind.eyewitness mind. The new database images should beThe new database images should be added correctly and the culprit recordadded correctly and the culprit record should be updated time to time.should be updated time to time. The system should be able to upload theThe system should be able to upload the image from database correctly.image from database correctly. The nearest match should be shown.The nearest match should be shown.
- 15. Non Functional RequirementsNon Functional Requirements:-:- SecuritySecurity - The database of criminals should be- The database of criminals should be highly secured and any modification by strangershighly secured and any modification by strangers should be strictly prohibited.should be strictly prohibited. PerformancePerformance - The performance of the proposed- The performance of the proposed system should be accurate and fast.system should be accurate and fast. Robust -Robust - The system should be robust andThe system should be robust and vigorous.vigorous. ReliableReliable - The system should be reliable and- The system should be reliable and should able to bear load.should able to bear load.