Morning: Image Database Retrieval | ||
Gatekeeper: Face detection and recognition | ||
Complex Feature Image Database Retrieval (Tieu) | ||
Flexible Template Retrieval (Yu) | ||
Interlude | ||
Video/Audio Source Separation (Fisher) | ||
Mathematical Expression Recognition (Matsakis) | ||
Lunch | ||
Visit Prof. Brooks lab |
Afternoon: Variable Viewpoint Reality | ||
Real-time 3D reconstruction of people (Snow) | ||
Automatic camera calibration (Snow + Lee) | ||
Tracking of articulate human models (Lee + Winn) | ||
Modeling of human dynamics (Viola + Fisher) | ||
Gatekeeper:
Receptionist
& Security
Greet guests | |
Direct people to their destinations | |
Recognize employees | |
Turn back unauthorized visitors |
Gatekeeper is a
constant observer…
Detecting faces is very difficult
Detecting and Recognizing Faces
Key Difficulty: Variation in Pose | ||
State of the art: generalized templates | ||
Neural Networks / Deformable Templates / etc. | ||
Templates have difficulty with pose variation… | ||
Rotation, scale, complex deformation | ||
Must reduce the dependence on relative pose. | ||
Approach: Detecting people as a statistical distribution of multi-scale features | ||
Statistical Distribution
of Multi-scale Features
Discrimination via Cross Entropy
Motivation: Finding vehicles in clutter
Can also be used for segmentation…
Key facial features
- determined automatically
- located automatically
New Face Recognition Algorithm
Measure the occurrence and location of “key” facial features. | ||
Facial identity depends both on the types of features and their location. | ||
Relation to Active Search… | ||
Match measure is a histogram of multiscale features | ||
Like color histogram, Active Search can be used... | ||