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

Original Research Paper
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Abstract

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.

Keywords

Multi-view face detector Background rejector Pose classifier Pose-specific face detector Face validator Order relation feature Doubly domain-partitioning classifier 

Notes

Acknowledgements

This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the SW Starlab support program (IITP-2017-0-00897) supervised by the Institute for Information and Communications Technology Promotion (IITP). Also, this research was supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Ministry of Science and ICT (MSIT) (IITP-2014-0-00059, Development of Predictive Visual Intelligence Technology).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPOSTECHPohangKorea

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