Two-stage cascade model for unconstrained face detection (CROSBI ID 638985)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Marčetić, Darijan ; Hrkać, Tomislav ; Ribarić, Slobodan
engleski
Two-stage cascade model for unconstrained face detection
In this paper, we propose a two-stage model for unconstrained face detection. The first stage is based on the normalized pixel difference (NPD) method, and the second stage uses the deformable part model (DPM) method. The NPD method applied to in the wild image datasets outputs the unbalanced ratio of false positive to false negative face detection when the main goal is to achieve minimal false negative face detection. In this case, false positive face detection is typically an order of magnitude higher. The result of the NPD-based detector is forwarded to the DPMbased detector in order to reduce the number of false positive detections. In this paper, we compare the results obtained by the NPD and DPM methods on the one hand, and the proposed two-stage model on the other. The preliminary experimental results on the Annotated Faces in the Wild (AFW) and the Face Detection Dataset and Benchmark (FDDB) show that the two-stage model significantly reduces false positive detections while simultaneously the number of false negative detections is increased by only a few.
Face; Detectors; Face detection; Silicon; Deformable models; Computational modeling; Feature extraction
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Podaci o prilogu
21-24.
2016.
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objavljeno
978-1-4673-8916-7
Podaci o matičnoj publikaciji
First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016
Zheng - Hua Tan
Aaalborg: Curran Associates
Podaci o skupu
First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016
predavanje
06.07.2016-08.07.2016
Aalborg, Danska