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Proceedings Paper

Adaptive WildNet Face network for detecting face in the wild
Author(s): Dinh-Luan Nguyen; Vinh-Tiep Nguyen; Minh-Triet Tran; Atsuo Yoshitaka
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Paper Abstract

Combining Convolutional Neural Network and Deformable Part Models is a new trend in object detection area. Following this trend, we propose Adaptive WildNet Face network using Deformable Part Models structure to exploit advantages of two methods in face detection area. We evaluate the merit of our method on Face Detection Data Set and Benchmark. Experimental results show that our method achieves up to 86.22% true positive images in 1000 false positive images in FDDB. Our method becomes one of state-of-the-art methods in FDDB dataset and it opens a new way to detect faces of images in the wild.

Paper Details

Date Published: 8 December 2015
PDF: 5 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750S (8 December 2015); doi: 10.1117/12.2229889
Show Author Affiliations
Dinh-Luan Nguyen, Univ. of Science, VNU-HCM (Viet Nam)
Vinh-Tiep Nguyen, Univ. of Science, VNU-HCM (Viet Nam)
Minh-Triet Tran, Univ. of Science, VNU-HCM (Viet Nam)
Atsuo Yoshitaka, Japan Advanced Institute of Science and Technology (Japan)


Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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