30-05-2012, 03:20 PM
FACE RECOGNITION
FACE RECOGNITION.pptx (Size: 374.38 KB / Downloads: 37)
INTRODUCTION
•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.
•Live face detection.
•By comparing selected facial features from the image and a facial database.
PROCESS
• Identify face features.
• Train the system with a convenient database.
facial recognition algorithms
• Extracting landmarks, features from an image of the subject's face.
• Analyze the relative position, size, shape of the eyes, nose, cheekbones, and jaw.
• Geometric, which looks at distinguishing features.
• Photometric, that distill an image into values and comparing the values with templates to eliminate variances.
ALGORITHMS
• Principal Component Analysis.
• Linear Discriminate Analysis.
• Elastic Bunch Graph Matching Fisher Face.
• Hidden Markov Model.
Principal Component Analysis
• Principal Component Analysis (PCA) is a known powerful technique under the broad title of factor analysis.
• Reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables).
• Prediction, redundancy removal, feature extraction, data compression, etc.
three-dimensional face recognition
• Claimed to achieve previously unseen accuracies.
• Uses 3-D sensors to capture information about the shape of a face.
• To identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin.
Advantage of 3-d
• It is not affected by changes in lighting like other techniques.
• It can also identify a face from a range of viewing angles, including a profile view.
• Even a perfect 3D matching technique could be sensitive to expressions.
PREPROCESSING
• Detect the face
• Enhance the image
• Identify nose and boundaries
DATABASE ORL DATABASE
• A set of pictures taken between 1992 and 1994 at Olivetti Research Laboratory.
• Different times, expressions, lightning and details
• 10 images per person
Yale Face Database B
• 165 images of 15 individuals.
• There are 11 images per subject, one per different facial expression.
applicability
• Recognize criminals.
• Simultaneous multiple face processing.
• In public spaces (airports, shopping centers).
• Verify identity to grant access in restricted areas.
• Holder of the passport is the rightful owner or not.
enhancements
• ATM would capture an image of your face, and compare it to your photo in the bank database to confirm your identity.
• By using a webcam to capture a digital image of yourself, your face could replace your password as a means to login.
drawback
• Pretty good at full frontal faces and 20 degrees off.
• Include poor lighting, sunglasses, long hair, other objects partially covering the subject’s face, and low resolution images.
• Another serious disadvantage is that many systems are less effective if facial expressions vary. Even a big smile can render in the system less effective.
Conclusion
• Already in use mainly for security an human-machine interface applications.
• Facial recognition system identifies the particular person and grant the access.