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Two-stage cascade model for unconstrained face detection (CROSBI ID 638985)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Marčetić, Darijan ; Hrkać, Tomislav ; Ribarić, Slobodan Two-stage cascade model for unconstrained face detection // First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016 / Zheng - Hua Tan (ur.). Aaalborg: Curran Associates, 2016. str. 21-24

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

Povezanost rada

Računarstvo