presented by:
Ames Brown & Jason Cherry

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Real-Time Facial Recognition
• Topics Of Discussion
• Why real-time face recognition?
• What is difficult about real-time face recognition?
• In general how is face recognition done?
• Eigenfaces
• Other face recognition algorithms
• Opposition
• Future of face recognition
• Why Real-Time Face Recognition?
• Security
– Fight terrorism
– Find fugitives
• Personal information access
– ATM
– Sporting events
– Home access (no keys or passwords)
– Any other application that would want personal identification
• Improved human-machine interaction
• Personalized advertising
• Beauty search
• Real-Time Face Recognition System Requirements
• Want the system to be inexpensive enough to use at many locations
• Match within seconds
– Before the person walks away from the advertisement
– Before the fugitive has a chance to run away
• Ability to handle a large database
• Ability to do recognition in varying environments
• What Is Difficult About Real-Time Face Recognition
• Lighting variation
• Orientation variation (face angle)
• Size variation
• Large database
• Processor intensive
• Time requirements
• General Image Types
• Still image (digital photograph)
– AmIU
• Dynamic image
– Video camera
• FERET database
• Contains images of 1,196 individuals, with up to 5 different images captured for each individual
• Often used to test face recognition algorithms
• Information on obtaining the database can be found here: General Face Recognition Steps
• Face Detection
• Face Normalization
• Face Identification
• Face Detection
• Face Detection: “Face Space”
• Face Detection: Motion Detecting and Head Tracking
• Face Detection: Motion Detecting and Head Tracking
• Face Normalization
• Adjustment
– Expression
– Rotation
– Lighting
– Scale
– Head tilt
• Eye location
• Face Normalization: FERET face2norm
1. Image is rotated to align the eyes (eye coordinates must be known).
2. The image is scaled to make the distance between the eyes constant. The image is also cropped to a smaller size that is nearly just the face.
3. A mask is applied that zeros out pixels not in an oval that contains the typical face. The oval is generated analytically.
4. Histogram equalization is used to smooth the distribution of gray values for the non-masked pixels.
5. The image is normalized so the non-masked pixels have mean zero and standard deviation one.
• Face Identification
• Application of a face recognition algorithm
• PCA Algorithms
• Principle Component Analysis
• Look at the principal components of the distribution of faces, or the eigenvectors of the covariance matrix of the set of face images
• Eigenfaces
• Eigenfaces Algorithm
Eigenfaces Recognition
1. Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces.
2. Determine if the image is a face at all by checking to see if the image is sufficiently close to “face space.”
3. If it is a face, classify the weight pattern as either a known person or as unknown.
4. (Optional) Update the eigenfaces and/or weight patterns.
• Eigenfaces Problems
• Recognition performance decreases quickly as the head size, or scale, is misjudged. The head size in the input image must be close to that of the eigenfaces for the system to work well
• In the case where every face image is classified as known, a sample system achieved approximately 96% correct classification averaged over lighting variation, 85% correct averaged over orientation variation, and 64% correct averaged over size variation
• Parameter Based Facial Recognition
• Facial image is analyzed and reduced to small set of parameters describing prominent facial features
• Major features analyzed are: eyes, nose, mouth and cheekbone curvature
• These features are then matched to a database
• Advantage: recognition task is not very expensive
• Disadvantage: the image processing required is very expensive and parameter selection must be unambiguous to match an individual’s face
• Template Based Facial Recognition
• Salient regions of the facial image are extracted
• These regions are then compared on a pixel-by-pixel basis with an image in the database
• Advantage is that the image preprocessing is simpler
• Disadvantage is the database search and comparison is very expensive
• Real-Time System using Template Recognition
• Implemented on a IBM PC w/ a video camera, image digitizer, and custom VLSI image correlator chip (340 Mop/second).
• Needed single frontal facial image under semi-controlled lighting conditions
• Took the system 2 to 3 seconds to identify a user from 173 images of 34 persons
• 88% recognition rate
• How the System Worked
• Artificial Neural Networks in Real-Time Face Recognition
• Use many of the same algorithms described before but with back propagation ANN’s
• Disadvantages: Complex and difficult to train
• Difficult to implement
• Sensitive to lighting variation
• There are many face recognition algorithms
• LDA (Linear Discriminant Analysis)
• Bayesian Classifier
• Gabor Wavelet Algorithm
• Elastic graphs
• Not Everyone Loves Face Recognition
• Critics say it produces too many false positives
• Invasion of privacy
• To easy to misuse for wrong purposes
• Technology is not accurate enough given the current technology and algorithms
• Future Of Face Recognition
• Some consider the problem impossible
• No standard way of approaching the problem
• Advancements in hardware and software
• Slow integration into society in limited environments
• Very large potential market
• Questions?
• Topics Of Discussion
• Why real-time face recognition?
• What is difficult about real-time face recognition?
• In general how is face recognition done?
• Eigenfaces
• Other face recognition algorithms
• Opposition
• Future of face recognition