Facial recognition technology by vaibhav


Published on

Published in: Technology, Business
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Facial recognition technology by vaibhav

  1. 2. A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition system.
  2. 4. Every face has at least 80 distinguishable parts called nodal points. Some of them are:
  3. 5. Every face has at least 80 distinguishable parts called nodal points. Some of them are: <ul><li>Distance between the eyes </li></ul>
  4. 6. Every face has at least 80 distinguishable parts called nodal points. Some of them are: <ul><li>Distance between the eyes </li></ul><ul><li>Width of the nose </li></ul>
  5. 7. Every face has at least 80 distinguishable parts called nodal points. Some of them are: <ul><li>Distance between the eyes </li></ul><ul><li>Width of the nose </li></ul><ul><li>Depth of eye sockets </li></ul>
  6. 8. Every face has at least 80 distinguishable parts called nodal points. Some of them are: <ul><li>Distance between the eyes </li></ul><ul><li>Width of the nose </li></ul><ul><li>Depth of eye sockets </li></ul><ul><li>Structure of cheek bones </li></ul>
  7. 9. Every face has at least 80 distinguishable parts called nodal points. Some of them are: <ul><li>Distance between the eyes </li></ul><ul><li>Width of the nose </li></ul><ul><li>Depth of eye sockets </li></ul><ul><li>Structure of the cheek bones </li></ul><ul><li>Length of jaw line </li></ul>
  8. 10. A general face recognition software conducts a comparison of these parameters to the images in its database. Depending upon the matches found, it determines the result. This technique is known as feature based matching and it is the most basic method of facial recognition.
  9. 11. A 3D facial recognition model provides greater accuracy than the feature extraction model. It can also be used in a dark surroundings and has a ability to recognize the subject at different view angles. Using 3D software, the system Goes through a number of steps to verify the identity of an individual.
  10. 12. Acquiring an image can be done through a digital scanning device. Once it detects the face, the system determines heads position, size and pose.
  11. 13. The system then measures the curves of the face on a sub-millimeter scale and creates a template. The system translates this template into a unique code.
  12. 14. The image thus acquired will be compared to the images in the data base and if 3D images are not available to the database, then algorithms used to get a straight face are applied to the 3D image to be matched. Finally in verification, the image is matched to only one image in the database and the result is displayed as shown alongside.
  13. 15. The most commonly used unique feature for facial recognition is iris of the eye. No two human beings, even twins have exactly similar iris.
  14. 16. Image acquisition Matching Segmentation Encoding Images obtained from CASIA. Employs Canny edge detector & Hough Transform . Normalization & use of Log-Gabor filter. HD calculation used for determining matches
  15. 17. SEGMENTATION: Involves locating the iris region and isolating it. ENCODING: Creating a template that contains the most discriminating features of the iris. MATCHING: Comparison of two iris templates and determining if they belong to the same individual
  16. 18. This system was a success as 93% of samples gave perfect recognition. Salient features of iris were identified, extracted, encoded then matched as desired. The only setback was the fact that Matlab processed the images slowly thus would not suit real time processing. APPLICATION: Immigration department. ATM identity verification.
  17. 19. -Principal Component Analysis using eigenfaces -Linear Discriminate Analysis -Elastic Bunch Graph Matching using Fisherface algorithm -Hidden Markov model -Neuronal motivated dynamic link matching.
  18. 21. The only way to overcome this challenge is better equipment, i.e. basically , use of high tech cameras. It is very much essential for the system to catch the image accurately.
  19. 24. The only way to overcome this challenge is better ALGORITHMS for facial recognitions. If the systems are programmed for every possible permutation and combination of the image, an accurate match can be achieved. Some algorithms that try to overcome this problem are as follows: -Half-face based algorithm -Local binary pattern -Neural network, etc.
  20. 25. <ul><li>The kind of equipment used for facial recognition depends upon the purpose of using the technology. A facial recognition technique may be used for the following purposes. </li></ul><ul><li>Domestic security systems </li></ul><ul><li>Police surveillance </li></ul><ul><li>Domestic Computer / phone identification systems </li></ul><ul><li>Employee management systems in Companies. </li></ul><ul><li>National and International security systems. </li></ul><ul><li>Let us consider equipment for basic police surveillance. </li></ul>
  21. 26. <ul><li>The facial recognition equipment used for basic surveillance purpose has 3 important components. </li></ul><ul><li>The camera or scanning device </li></ul><ul><li>Infrared illuminator </li></ul><ul><li>An efficient software </li></ul>
  22. 27. An IR-illuminator is a device that emits infrared light-low frequency electromagnetic radiation that's outside the visible spectrum. In other words, it gives off light that a camera can pick up and use, but that a person can't see-so while it's still dark to the human eye, the camera can see just fine . There are 3 main types of infrared illuminators namely diodes, lamps & lasers. IR-illuminator Without IR-illuminator
  23. 28. Other FR equipments generally use variations in software. For a domestic Computer user recognition system, a basic 2D face recognition algorithm is sufficient. With some basic verifications, the user of the phone or Computer can be identified. Complex algorithms and equipment are used in cases of national and international security issues.
  24. 29. Facial recognition is a very useful mechanism when it comes to office related user identification. One such example is a product of Havon industries called FaceID. Face ID is industry first embedded facial recognition system with leading “Dual Sensor” Facial Recognition Algorithm, which designed for application like physical access control and time attendance, identity management and so on.
  25. 30. In around 10 years from now, it is being estimated that facial recognition technology will be the backbone of all major security, home and networking service. With the growth of social networking over the web, unbelievably accurate facial recognition algorithms and advanced equipment, a person’s face, no mater ageing or disguises or damage, can be recognized and data about that person can be produced. Here is a small glimpse of what facial recognition technology would make of the future social networking….
  26. 31. Assuming amazing resolutions, facial and object recognition, this is how social networking would be in the future….
  27. 32. Pros: 1.Better security systems. 2.Easy user verifications. 3.Greatly reduces current load on security and judicial systems. Cons: 1.Privacy issues. 2.Even helps in kidnapping! 3.Errors in detection may cause inconvenience to innocent users.
  28. 34. 1. W. Bledsoe. Man-machine facial recognition 2. T. Kanade Computer Recognition of Human Faces 3. www.wikipedia.com 4. www.authorstream.com 5.www.howstuffworks.com 6. www.inttelix.com 7. www.face-rec.org 8. www.facialrecognitionsolution.com 9. www.facedetection.com