RealTime Facial Recognition MATCH EECE 279 RealTime Systems

Real-Time Facial Recognition MATCH! EECE 279: Real-Time Systems Design Vanderbilt University Ames Brown & Jason Cherry

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) – Am. IU • 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: http: //www. itl. nist. gov/iad/humanid/feret/

General Face Recognition Steps • Face Detection • Face Normalization • Face Identification

Face Detection In General • Locate face in a given image • Separate it from the scene Different Approaches • Motion detecting and head tracking • “Face Space” distance

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 face 2 norm 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 Initialization 1. Acquire an initial set of face images (the training set)

Eigenfaces Algorithm 2. Calculate the eigenfaces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated

Eigenfaces Algorithm 3. Calculate the corresponding distribution in M-dimensional weight space for each known individual, by projecting their face images onto the “face space. ”

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-bypixel 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 semicontrolled 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 Get more information and source code at http: //www. cs. colostate. edu/evalfacerec/index. html

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?

References • M. Turk and A. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 1991 • C. Nastar and M. Mitschke. Real-Time Face Recognition Using Feature Combination. In Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, April 1998 • J. Gilbert and W. Yang. A Real-Time Face Recognition System using Custom VLSI Hardware. Harvard Undergraduate Honors Thesis in Computer Science, 1993.

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

Real-Time Facial Recognition MATCH! EECE 279: Real-Time Systems Design Vanderbilt University Ames Brown & Jason Cherry