III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.In this stage, four parameters are computed whichdefine an ellipse (center position, orientation and sizesof the semi-axis) for all relevant ellipses in the image.Here, the ellipses are considered to be the possible faceregions (final product of this stage). This detectionmethod is based in the Hough Transform and it usesthe convexity property computed in the preprocessingstage. For this stage, the following steps are applied tothe resultant edge image produced in the previous stage(based on [1]): Apply the stream scan process [1] (Fig. 2); Compute the intersections and search for thecenter of the ellipses (Fig. 3); Vote for semi-axis ratio and rotation using anand accumulator array; Compute the sizes of the semi-axis.The stream scan restricts the number of point pairsassociated to the same ellipse considering restrictionsas the associated convexity, a minimum distancebetween them and an angle around 90º, reducing thecomputational complexity. More details can be foundin [1].4. Classification using MLPFigure 2. Directions and associatedconvexity used by the stream scanprocess. [1]After selection of possible face regions, we stillneed to determine which of these regions in factcorrespond to faces.To accomplish the classification process, theselected region was rescaled, rotated and applied toMultilayer Perceptron Networks (MLP) [6] usingCBCL Face Database #1 [7] from MIT (MassachussetsInstitute of Technology) for both train and test steps.This database is divided in train images and testimages. The train set contain 2429 faces and 4548 nonfacessamples while the test set contain 472 faces and23573 non-faces samples. All grayscale images are19x19 pixels with face images, with pose andillumination variations.The train step is uses the backpropagationalgorithm, with the activation function of theintermediate layers being a bipolar sigmoid, while theactivation function of the final layer (with only onenode) is the bipolar sigmoid and the degree function inseries. For the degree function is considered the value1 for face and value -1 for non-face.The test step is applied on the whole test set in orderto evaluate the performance when using the MLP tosolve the classification problem. If the resultclassification process was -1 the region selected inellipse detection step is rejected of the detectionprocess.At the end of the process (ellipse detection andclassification steps), the faces are detected, as well astheir positions and dimensions (in the ellipse detectionstep).5. ResultsThis method was applied to several images ofdistinct size, scale, and rotation, and also underdifferent illumination conditions. The qualityevaluation was accomplished subjectively for ellipsedetection stage, that is, visually the center points of theellipses are close to the expected face centers and forall test images the detected center points had occurredon the faces (Fig. 4). The Fig. 5 shows samplesextracted regions after ellipse detection step. Theresults are considered very satisfactory. Moreover, thismethod can be used in real-time applications.The ellipse detection step is fast and leads, usually,to the correct center points. Depending on the choicefor the parameters (such as minimum distance andwindow size) of the ellipse detector, in some images alot of candidate faces are selected. Thus, the parameteradjusts are somewhat problem dependent. Thispossible aggravation is a dependency on the edgedetector and leads to an increase in the processingtime.Figure 3. Selected point pairs. [1]272