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We aim to design a general learning framework for face detection while handling some problems caused by a variety of variations in images, including Profile, Rotation, Occlusion, Lighting Conditions, Varied Expressions, Multiple Faces and Scales. We are motivated to formulate the task as a classification problem over data of multiple classes. Our approach takes advantage of a multi-class boosting algorithm, MBHboost, to effectively perform face detection with the assistance of its integration with a cascade structure. As a result, it features great flexibility in the sense that only one single boosted cascade is needed without worrying about how to select the most appropriate cascade for the detection.
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Real-time, Multi-view Face Detection
- A real-time, multi-view face detector
- Multi-view: Profile faces, rotated faces, faces with partial occlusions, or faces under different lighting conditions
- Real-time: At least 15 frames per second on a PC
- Two key components
- A new boosting algorithm: Multi-class Bhattacharyya boost (MBHBoost)
- A new detection architecture: Multi-class cascade
- The detector leads to a computational cost sub-linear to the number of face classes (views)
Key Features / Techniques
- Vector-valued weak learners
- A new boosting algorithm: Multi-class Bhattacharyya boost (MBHBoost)
- A new detection architecture: Multi-class cascade
Rectangle Features
Problems Caused by Thresholding and Our Solution
Classifier Sharing
- An vector-valued weak learner associated with is defined by
- Advantages
- Shared by all classes, no human knowledge or searching
- Each component independently learns a decision boundary
- Computational efficiency: The value of k is identical in all components
Multi-class Cascade
- Reduce the detection problem to a series of pattern rejection problem
- Speed up the detection process: Coarse-to-fine detection
- Deal with vector-valued outputs: Message-passing between stages
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