Efficient and accurate face detection using heterogeneous feature descriptors and feature selection

The performance of an efficient and accurate face detection system depends on several issues: (1) distinctive representation for face patterns; (2) effective algorithm for feature selection and classifier learning; (3) suitable framework for rapid background removal. To address the first issue, we propose to represent face patterns with a set of heterogeneous and complementary feature descriptors including the Generalized Haar-like (GH) descriptor, Multi-Block Local Binary Patterns (MB-LBP) descriptor and Speeded-Up Robust Features (SURF) descriptor. To address the second issue, Particle Swarm Optimization (PSO) algorithm is incorporated into the Adaboost framework, replacing the exhaustive search used in original Adaboost for efficient feature selection. The utilization of heterogeneous feature descriptors enriches the diversity of feature types for Adaboost learning algorithm. As a result, classification performance of the boosted ensemble classifier also improves significantly. A three-stage hierarchical classifier structure is proposed to tackle the last issue. In particular, a new stage is added to detect candidate face regions more quickly by using a large size window with a large moving step. Nonlinear support vector machine (SVM) classifiers are used instead of decision stump classifiers in the last stage to remove those remaining complex non-face patterns that cannot be rejected in the previous two stages. Combining the abovementioned effective modules, we derive the proposed Hetero-PSO-Adaboost-SVM face detector that achieves superior detection accuracy while maintaining a low training and detection complexity. Extensive experiments demonstrate the robustness and efficiency of our system by comparing it with several popular state-of-the-art algorithms on our own test set as well as the widely used CMU+MIT frontal and CMU profile face dataset.