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FACIAL RECOGNITION SYSTEM Submitted By: Biswa Ranjan Patra ENTC 0401229099 OVERVIEW People have an amazing ability to recognize and remember thousands of face. In this seminar we will see how computers are turning our face into computer code so it can be compared to thousands of other faces. Facial recognition software can be used to find criminals in a crowd, turning a mass of people into a big lineup. DEFINITION Facial Recognition Systems are computer based security systems that are able to automatically detect and identify human face. The automatic access control system on the basis of modern biometrics methods of identification of face recognition. System Snapshots Identification Verification EARLY DEVELOPMENT Pioneers of Automated Facial Recognition include: Woody Bledsoe, Helen Chan Wolf & Charles Bisson. During 1964 & 1965, Bledsoe, along with Helen Chan & Charles Bisson, worked on using the computer to recognize human faces. This project was labeled Man-Machine because the human extracted the coordinates of a set of features from the photographs, which were then used by computer for recognition. THE FACE Your face is an important part of who you are and how people identify you. Imagine how hard it would be to recognize an individual if all faces looked the same. Except in the case of identical twins, the face is arguably a person’s most unique physical characteristics. While humans have had the innate ability to recognize & distinguish different faces for millions of years, computers are just now catching up. If you look in the mirror, you can see that your face has certain distinguishable landmarks. These are peaks and valleys that make up facial features. Visionics defines these landmarks as NODAL POINTS. There are about 80 Nodal Points on human face. Here are a few of the Nodal Points that are measures by the software: ► Distance between eyes ► Width of the nose ► Depth of eye sockets ► Cheek bones ► Jaw lines ► Chin SOFTWARE Facial Recognition Software is based on the ability to first recognize faces, which is a technological feat in itself & then measure the various features of each face. The software that are used are FaceIt and FRS SDK Ver2.0 Facial recognition software is designed to pinpoint a face and measure its features. THE BASIC PROCESS 1) Detection: When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. 2) Alignment: Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it. 3) Normalization: The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process. 4) Representation: The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data 5) Matching: The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation The heart of the FaceIt facial recognition system is the Local Feature Analysis (LFA) algorithm. This is the mathematical technique the system uses to encode faces. The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database. Each faceprint is stored as an 84-byte file. Using facial recognition software, police can zoom in with cameras and take a snapshot of a face. MULTI MODAL SYSTEM FOR LOCATING HEADS AND FACES For a head location system to be perceived as truly non-intrusive by the observed people, a free motion of the heads has to be permitted. This results in large variations of the heads' sizes and orientations. To handle such a large range of conditions efficiently, we combine the information of three channels: shape, color and motion. This has resulted in a robust face and head location system that is suited for such applications as tracking people for surveillance purposes, model-based image compression for video telephony , and intelligent computer-user interfaces. SHAPE ANALYSIS The shape analysis tries to find outlines of heads or combinations of facial features that indicate the presence of a face. It uses luminance only, and therefore can even work with cheap monochrome cameras. For frontal view of faces, we first identify candidate areas for various facial features, and then we search for combinations of such areas to find the whole face. Example of the facial feature detection process. The top-left image is the original, the top-right image has been filtered to select a range of spatial frequencies and sizes. The bottom-left shows the image after adaptive thresholding where areas of interest have been marked with connected areas of gray pixels. The bottom right image shows the best combination of facial features that could be identified: eyebrows, eyes and nostrils. FILTERING An image is first transformed by two filters, the first one is a bandpass filter selecting a range of spatial frequencies. The second one is tuned to detect a range of sizes of a simple shape such as a rectangle or an ellipse. These processing steps reduce variations due to different lighting conditions and enhance areas of facial features or head boundaries. The band-pass filter eliminates slowly varying gradients in light intensity and reduces differences in intensities between the two sides of a face in the case of side illumination. Examples of head locations, as well as eye and mouth locations, found with the shape analysis. In the face on the left only the search for the head outline succeeded since the search for facial features is limited to a head tilt of +-30 degrees. THRESHOLDING After the filtering operations, the image is thresholded with an adaptive thresholding technique. The goal is to identify individual facial features with a simple connected component analysis. n-GRAM SEARCH Once candidate facial features are marked with connected components, combinations of these features, that could represent a face, have to be found. This is done with an 'n-gram' search. First the shape of each individual connected component is analyzed and those that can definitely not represent a facial feature are discarded. Then combinations of two connected components are tested whether they can represent a combination of two facial features, for example an eye pair, eye brows, or an eye and a mouth. In the next step triple combinations are evaluated, etc. Marking areas of interest for identifying the positions of facial features. The top two images have been filtered for spatial frequencies and sizes and are binarized with two different thresholds. COLOUR ANALYSIS Color information is an efficient tool for identifying facial areas and specific facial features if the system can be calibrated properly for particular conditions. We can use the color information to track a face or a facial part, such as the lips. As color space we chose normalized rgb values [r = R/(R+G+B), g = G/(R+G+B), b = B/(R+G+B)] Example of the color analysis. The top left image is the original and the top right the downsampled representation, where different hue values have been transformed into different gray levels. The bottom right image is the segmented image, where the colors inside the ellipse of the diagram at the bottom right were taken as skin colors. The bottom right diagram shows the distribution of the image colors in the r-g plane. Many of the background colors are very similar to the face colors and without calibration face and background could not be distinguished. MOTION ANALYSIS If multiple images of a video sequence are available, motion is often a parameter that is easily extracted and offers a quick way for locating objects, such as heads. When a close-up view of a talking person is analyzed, motion usually leads quickly to the area around the mouth, because this is the part of a face that moves the fastest. The first step in the algorithm is to compute the absolute value of the differences in a neighborhood (typically 8x8 pixels) surrounding each pixel. When the accumulated difference is above a predetermined threshold T, we classify the pixel as belonging to a moving object. Example of the motion analysis. The white outline shows the boundary of a moving object. The cross marks the center of the head. Examples of heads located with the motion detection algorithm. POTENTIAL USES By enforcement agencies to capture random faces in crowds. Eliminating voter fraud Check-cashing identity verification Computer security ATM and Cash-Checking Security Many people who don't use banks use check cashing machines. Facial recognition could eliminate possible criminal activity. Facial recognition software can be used to lock your computer. CONCLUSION While humans have had the innate ability to recognize and distinguish different faces for millions of years, computers are just now catching up. To identify someone, facial recognition software compares newly captured images to databases of stored images. The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation. While facial recognition can be used to protect your private information, it can just as easily be used to invade your privacy by taking you picture when you are entirely unaware of the camera. Thank You..!
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