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.Figure 4. Sample images with ellipse centers detectedFigure 5. Sample extracted images of ellipsedetection stepIn the last step, in order to classify each detectedellipse area in the image as face or non-face, a MLPneural network was applied. This neural architecture isdemonstrated to suffer with small displacements androtations in database faces images, what bringsdifficulty to the generalization process.In the tests, the top result considering only imageswith a face was 37.5%, which is a low rate. This isexplained by sensitivity to displacements and rotationsof MLP and the fact that minor adjustments to correctmainly displacements are not yet implemented. Thisproblem can be solved applying a preprocessing asblurring to train and test images, as well as normalizethe candidate face subimage according to a symmetryaxis.6. Conclusions and Future WorksIn this paper, a face detection method based onselecting possible face regions through a robust andfast ellipse detector, and the their classification as faceor non-face using a neural network was proposed.In first part, the major problem is the number of theselected candidate points for the ellipse detectionprocess. The procedure is sensitive to some parameteradjustments, but they can be tuned by restricting theapplication scope (mainly scale).In second part, a previously trained MLP is appliedto selected regions. However, the most criticalproblems in using the MLP are its sensitivity for thedisplacement and rotation properties. Thus, apreprocessing is necessary to soften the small rotationsand displacement in input images. This is the mainreason for most of the lower classification resultsobtained so far. Corrections on this matter arefortunately a non-complex task, and we are working onthem.In spite of related difficulties, the proposed methodin this paper is demonstrated be very promising forsolving the face detection problem, with reliability andtime efficiency.For future work and improvements we suggest:1) replace the MLP by a convolutional neuralnetworks, such as the Neocognitron by Fukushima [8](which deal properly with displacement and rotationproperties), and 2) refining the ellipse detectionmethod by adding more previous knowledge to reducethe number of selected points and, therefore,decreasing the computational cost.7. AcknowledgementsThis work is being supported by FAPESP (process2005/03671-7).8. References[1] S. C. Zhang, and Z. Q. Liu, “A robust, real-time ellipsedetector”, Pattern Recognition, vol. 38, pp. 273-287, 2005.[2] A. Gavioli, M. Biajiz, and J. Moreira, “MIFLIR: A MetricIndexing and Fuzzy Logic-based Image Retrieval System”,IEEE International Workshop on Managing Data forEmerging Multimedia Applications, Tokyo: IEEE, 2005.[3] A. Oriani and J. Moreira, “Preliminary Study ofExtraction of Facial Geometric Measures as Features forContent-Based Retrieval”, XVIII Brazilian Symposium onComputer Graphics and Image Processing, SIBGRAPI’2005,Natal, Brazil, 2005.[4] F. M. Alzahrani and T. Chen, “A real-time edge detector:Algorithm and VLSI architecture”, Real-Time Imaging, vol.3, pp. 363-378, 1997.[5] N. Otsu, “Threshold Selection Method from Gray-LevelHistograms”, IEEE Transactions on Systems Man andCybernetics, vol. 9, pp. 62-69, 1979.[6] Braga, A. P., T. B. Ludermir, et al., “Redes NeuraisArtificiais: Teoria e aplicações”, LTC - Livros Técnicos eCientíficos Editora S.A., v. 1, Rio de Janeiro: 2000, 262 p.[7] MIT Center for Biological and Computation Learning,“CBLC Face Database #1”. In:http://www.ai.mit.edu/projects/cbcl[8] Fukushima, K., “Neocognitron: A hierarchical neuralnetwork capable of visual pattern recognition”, NeuralNetworks, v. 1, n. 2, pp. 119-130, 1988.273