000174691 001__ 174691
000174691 005__ 20190509132417.0
000174691 0247_ $$2doi$$a10.5075/epfl-thesis-5325
000174691 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis5325-2
000174691 02471 $$2nebis$$a6853586
000174691 037__ $$aTHESIS
000174691 041__ $$aeng
000174691 088__ $$a5325
000174691 245__ $$aAlternative Search Techniques for Face Detection Using Location Estimation and Binary Features
000174691 269__ $$a2012
000174691 260__ $$bEPFL$$c2012$$aLausanne
000174691 300__ $$a163
000174691 336__ $$aTheses
000174691 520__ $$aThe sliding window approach is the most widely used  technique to detect objects from an image. In the past few  years, classifiers have been improved in many ways to  increase the scanning speed. Apart from the classifier design  (such as the cascade), the scanning speed also depends on a  number of different factors (such as grid spacing, and scale  at which the image is searched). Scanning grid spacing  controls the number of subwindows being processed, thus  controlling the speed of detection. When the scanning grid  spacing is larger than the tolerance of the trained  classifier it can suffer from low detections. In this thesis,  we propose an alternative search technique, which can improve  the detections when lesser number of subwindows are  processed. First, we present a technique to reduce the number of miss  detections while increasing the grid spacing when using the  sliding window approach for object detection. This is  achieved by using a small patch to predict the location of an  object within a local search area. To achieve speed, it is  necessary that the time taken for location prediction is  comparable or better than the time it takes in average for  the object classifier to reject a subwindow. We use binary  features and a decision tree as it proved to be efficient for  our application. In the process we also propose a variation  of an existing binary feature (Ferns) with similar  performance, and requires only half the number of pixel  access when compared to Fern feature. We analyze the effect  of patch size on location estimation and also evaluate our  approach on several face databases. Experimental evaluation  shows better detection rate and speed with our proposed  approach for larger grid spacing (lesser number of  subwindows) when compared to standard scanning technique. We also show that by using a simple interest point  detector based on quantized gradient orientation, as the  front-end to the proposed location estimation technique, we  can achieve better performance even when fewer number of  subwindows are processed. The interest points detected can be  assumed as a non-regular grid compared to regular grid in the  sliding window framework. A few image patches are sampled  around an interest point for estimating the probable face  location and further verified using a strong face classifier.  Experiment results show that using an interest point detector  can reduce the number of subwindows processed while  maintaining a good detection rate.
000174691 6531_ $$aface detection
000174691 6531_ $$alocation estimation
000174691 6531_ $$adecision tree
000174691 6531_ $$abinary features
000174691 6531_ $$ainterest point
000174691 6531_ $$adétection des visages
000174691 6531_ $$aestimation de la position
000174691 6531_ $$aarbre de décision
000174691 6531_ $$acaractéristiques binaires
000174691 6531_ $$apoints d'intérêt
000174691 700__ $$0243352$$g180677$$aBala Subburaman, Venkatesh
000174691 720_2 $$aBourlard, Hervé$$edir.$$g117014$$0243348
000174691 720_2 $$aMarcel, Sébastien$$edir.$$g143942$$0243994
000174691 8564_ $$uhttps://infoscience.epfl.ch/record/174691/files/EPFL_TH5325.pdf$$zTexte intégral / Full text$$s5258086$$yTexte intégral / Full text
000174691 909C0 $$xU10381$$0252189$$pLIDIAP
000174691 909CO $$pthesis$$pthesis-bn2018$$pDOI$$ooai:infoscience.tind.io:174691$$qDOI2$$qGLOBAL_SET$$pSTI
000174691 918__ $$dEDEE$$cIEL$$aSTI
000174691 919__ $$aLIDIAP
000174691 920__ $$b2012
000174691 970__ $$a5325/THESES
000174691 973__ $$sPUBLISHED$$aEPFL
000174691 980__ $$aTHESIS