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{"references": ["M. H. Yang, and D. J. Kriegman, \"Detecting Faces in Images: A\nSurvey\", IEEE Transactions on Pattern Analysis and Machine\nIntelligence, Vol. 24, No. 1, Jan. 2002, pp. 34 - 58.", "E. Hjelm\u251c\u00d1s, and B. K. Low, \"Face Detection: A Survey\", Computer\nVision and Image Understanding, Vol. 83, No. 3, Sept. 2001, pp. 236-\n274.", "H. Rowley, S. Baluja, and T. Kanade, \"Neural Network-Based Face\nDetection\", IEEE Transactions on Pattern Analysis and Machine\nIntelligence, Vol. 20, No. 1, Jan. 1998, pp. 23-38.", "H. Rowley, S. Baluja, and T. Kanade, \"Rotation Invariant Neural\nNetwork-Based Face Detection\", Proc. IEEE Conf. on Computer Vision\nand Pattern Recognition, 1998, pp. 38-44.", "H. Schneiderman, and T. Kanade, \"A Statistical Method for 3D Object\nDetection Applied to Faces and Cars\", Proc. IEEE Conference on\nComputer Vision and Pattern Recognition, Vol. 1, 2000, pp. 746-751.", "H. Kruppa, M. A. Bauer, and B. Schiele, \"Skin Patch Detection in Real-\nWorld Images\", Proc of the DAGM-Symposium, 2002, pp. 109-116.", "P. Viola and M. Jones., \"Robust real-time face detection\", International\nJournal of Computer Vision (IJCV) 57(2), 2004, pp. 137-154.", "T. Theocharides, G. Link, N. Vijaykrishnan, M. J. Irwin, and W. Wolf,\n\"Embedded Hardware Face Detection\", Proc. 17th International\nConference on VLSI Design, Jan. 2004, p.133.", "A. C. Loui, C. N. Judice, and S. Liu, \"An Image Database for\nBenchmarking of Automatic Face Detection and Recognition\nAlgorithms\", Proc. IEEE Conference on Image Processing, Vol. 1, 1998,\npp.146-150.\n[10] P. Sharma, and R.B. Reilly, \"A Color Face Image Database for\nBenchmarking of Automatic Facial Detection Algorithms\", Proc. 4th\nEuropean Conference of Video/Image Processing and Multimedia\nCommunications, July 2003, pp, 423 - 428.\n[11] P. J. Phillips, P. Rauss, and S. Der, \"FERET (Face Recognition\nTechnology) Recognition Algorithm Development and Test Report\",\nTechnical Report ARL-TR 995, U.S. Army Research Laboratory,\nOctober 1996.\n[12] K-K. Sung, and T. Poggio, \"Example-Based Learning for View-based\nHuman Face Detection\", IEEE Transactions on Pattern Analysis and\nMachine Intelligence, Vol. 20, No. 1, Jan. 1998, pp. 39-51.\n[13] A. Martinez, and R. Benavente, \"The AR Face Database,\" Technical\nReport CVC 24, Purdue Univ., 1998.\n[14] R-L. Hsu, and M. Abdel-Mottaleb, \"Face Detection in Colour Images\",\nIEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.\n24, No. 5, May 2002, pp. 696 - 706.\n[15] C. Garcia, and G. Tziritas, \"Face Detection Using Quantized Skin\nColour Regions Merging and Wavelet Packet Analysis\", IEEE\nTransactions on Multimedia, Vol. 1, No. 3, September 1999, pp. 264 -\n277.\n[16] C. Lin, \"Face Detection by Colour and Multilayer Feedforward Neural\nNetwork\", Proc. IEEE International Conference on Information\nAcquisition, June-July 2005, pp. 518-523.\n[17] K. Sandeep and A.N. Rajagopalan,\"Human Face Detection in Cluttered\nColor Images using Skin Color and Edge Information\", Proc. Indian\nConference on Computer Vision, Graphics and Image Processing, Dec.\n2002.\n[18] U.S. Census Bureau, 2000 Census of Population, Public Law 94-171\nhttp://factfinder.census.gov/.\n[19] Y. Wei, X. Bing, and C. Chareonsak, \"FPGA Implementation of\nADABOOST Algorithm for Detection of Face Biometrics\", Proc. IEEE\nInternational Workshop on Biomedical Circuits and Systems, Dec. 2004,\npp. S1/6.17-20.\n[20] E. Osuna, R. Freund, and F. Girosit, \"Training Support Vector\nMachines: An Application to Face Detection\", Proc. IEEE Computer\nSociety Conference on Computer Vision and Pattern Recognition, June\n1997, pp. 130-136.\n[21] Yongmen Li, S. Gong, and H. Liddell, \"Support Vector Regression and\nClassification-based Multi-view Face Detection and Recognition\", Proc.\n4th IEEE International Conference on Automatic Face and Gesture\nRecognition, March 2000, pp. 300-305.\n[22] Adobe Photoshop CS2, http://www.adobe.com/products/photoshop/.\n[23] O. Bernier, M. Collobert, R. Feraud, V. Lemaire, J. E. Viallet, and D.\nCollobert, \"MULTRAK: A System for Automatic Multiperson\nLocalization and Tracking in Real-time\", Proc. International Conference\non Image Processing, ICIP 98. Vol. 1, Oct. 1998, pp. 136-140.\n[24] S.Z. Li and Z. Zhang, \"FloatBoost Learning and Statistical Face\nDetection\", IEEE Transactions on Pattern Analysis and Machine\nIntelligence, Vol. 26, Issue 9, Sept. 2004, pp. 1112-1123.\n[25] S.Z. Li, L. Zhu, Z. Zhang, and H.-J. Zhang, \"Learning to Detect Multiview\nFaces in Real-time\", Proc. 2nd International Conference on\nDevelopment and Learning, June 2002, pp. 172-177.\n[26] Microsoft Office Excel, http://office.microsoft.com/enus/\nexcel/default.aspx.\n[27] Microsoft Office Access, http://office.microsoft.com/enus/\naccess/default.aspx.\n[28] Matlab Online, http://www.mathworks.com/products/matlab/.\n[29] T. Theocharides, N. Vijaykrishnan, and M. J. Irwin, \"A Parallel\nArchitecture for Hardware Face Detection\", Proc. IEEE Computer\nSociety Annual Symposium on Emerging VLSI Technologies and\nArchitectures, March 2006.\n[30] Computer Vision laboratory, Faculty of Computer and Information\nScience, University of Ljubljana, Slovenia, http://www.lrv.fri.unilj.\nsi/facedb.html."]}
This paper presents a new color face image database for benchmarking of automatic face detection algorithms and human skin segmentation techniques. It is named the VT-AAST image database, and is divided into four parts. Part one is a set of 286 color photographs that include a total of 1027 faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race. Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use. In this paper, descriptions of the database development, organization, format as well as information needed for benchmarking of algorithms are depicted in detail.
skin segmentation., color image analysis, Image database, facedetection
skin segmentation., color image analysis, Image database, facedetection
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