Facial Recognition CSE 391 Kris Lord

Background Face recognition is one of the fundamental problems in pattern analysis Difficulties arise due to large variation in facial appearance, head size, orientation and change in environmental conditions Computerized face recognition system still cannot achieve a completely reliable performance

Main Issues Often in practical situations, recognition must be achieved in real-time so efficiency and speed are crucial Variance in lighting, angles, and other environmental areas make recognition more of a problem to deal with May be hard to obtain a complete database of a population’s faces in optimal posture/lighting for processing False positives/inability to recognize a face still common in current state of algorithms Storage space a large issue, especially when dealing with matrix-based algorithms (the more detailed the picture, the larger the storage space needed)

3 Main Steps Face detection - Facial area is singled out and removed for processing within a noisy image Face normalization - Facial image is processed to counteract posture issues such as tilt, angle, lighting, and other environmental noise Face verification/recognition - Facial features are analyzed via a recognition algorithm to determine a match with an existing face in a database

“Eigenfaces” Approach Patterns, in the domain of facial recognition could be the presence of some objects (eyes, nose, mouth) in a face as well as relative distances between these objects. These characteristic features are called eigenfaces in the facial recognition domain (or principal components generally). They can be extracted out of original image data by means of a mathematical tool called Principal Component Analysis (PCA). Each eigenface represents only certain features of the face. If the feature is present in the original image to a higher degree, the share of the corresponding eigenface in the ”sum” of the eigenfaces should be greater. In order to cut down on large computational processing, only eigenfaces with the highest value (most characteristic facial features) are kept for processing

Common “Eigenface” Algorithm A set of training data (pictures of faces) are transformed into a set E of Eigenfaces Afterwards, the weights are calculated for each image of the training set and stored in the set W Upon observing an unknown image X, the weights are calculated for that particular image and stored in the vector WX. Afterwards, WX is compared with the weights of images, of which one knows for certain that they are faces (the weights of the training set W) If this average distance exceeds some threshold value , then the weight vector of the unknown image WX lies too ”far apart” from the weights of the faces. In this case, the unknown X is considered to not a face. If it is considered to be a face, its weight vector WX is stored for later classification, where it can be tested against specific images and their eigenfaces.

Success rate? Some algorithms are much more successful than others Success rate depends greatly on database of faces used Rate can vary considerably if databases are combined (“eigenface” success rate drops considerably, to 66% with combined databases)

Practical Applications Combat Terrorism/Airport Security Large event (e.g. Superbowl) security – ability to scan the crowd with a video camera and match against a database of criminal records Eliminate fake IDs Eliminate identity theft (ATMs) Casino security Tailored (personalized) advertisements of the future Online dating profiling

Current State of the Art Neural Net algorithms Elastic matching algorithms NEC Developed 3D face recognition algorithm with over 96.5% recognition rate under bad environmental conditions