An accurate and real-time multi-view face detector using ORFs and doubly domain-partitioning classifier

• Published in: Journal of real-time image processing Vol. 16; no. 6; pp. 2425 - 2440
• Main Authors: Yoon, Jongmin, Kim, Daijin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2019; Springer Nature B.V
• Subjects: Accuracy; Classification; Classifiers; Computer Graphics; Computer Science; Detectors; Discriminant analysis; Embedded systems; Image Processing and Computer Vision; Multimedia Information Systems; Original Research Paper; Partitioning; Pattern Recognition; Sensors; Signal,Image and Speech Processing; Pose-specific face detector; Order relation feature; Background rejector; Multi-view face detector; Doubly domain-partitioning classifier; Pose classifier; Face validator
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-018-0751-6

We propose a novel multi-view face detector that operates accurately and fast in challenging environments. It consists of four consecutive functional components: background rejector, pose classifier, pose-specific face detectors, and face validator. The background rejector removes non-face patches quickly, the pose classifier estimates poses of the surviving patches, one or more selected pose-specific face detectors according to their estimated pose labels determine that a given patch is a face by using winner take all (WTA) strategy, and the face validator checks whether the face-like patch is really a face. For achieving strong discrimination power with low computing overhead, we devise several types of order relation features (ORF) that encode the order relation among feature elements as a unique code. The devised ORFs are placed in functional components appropriately to ensure fast operation of the multi-view face detector. For accurate classification, we propose a doubly domain-partitioning (DDP) classifier that consists of a coarse domain-partitioning weak classifier followed by a fine bin-partitioning weighted linear discriminant analysis (wLDA) classifier. For fast classification, we devise a feature sharing method that shares identical features between the background rejector and the pose classifier, and among all classes in the pose classifier. We evaluated the proposed multi-view face detector using the FDDB, AFW, and PASCAL face datasets. The experimental results show that the proposed multi-view face detector outperforms other state-of-the-art methods in terms of detection accuracy and execution time.