Wednesday, April 14, 2021


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.


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




Robust Face Detection with Multi-class Boosting

Yen-Yu Lin and Tyng-Luh Liu

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2005



Fast Object Detection with Occlusions

Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh

European Conference on Computer Vision (ECCV), May 2004