{"references": ["Abu Sayeed Md. Sohail and Prabir Bhattacharya, \"Detection of Facial\nFeature Point Using Anthropometric Face Model\", Signal Processing\nfor Image Enhancement and Multimedia Processing, Multimedia\nSystem and Application, Volume 31,Part III, 2008.", "Udeni Jaysinghe & Anuja Dhrmaratne, \"Matching Facial Image using\nAge Related Morphing Changes\", World Academy of Science,\nEngineering and Technology 06, 2009.", "M. Maghami, R. Zoroofi, B. Araabi, M. Shiva and E. Vahedi, \"Kalman\nFilter Tracking for Facial Expression Recognition using Noticeable\nFeature Selection\", ICIAS, pp. 587-590, Nov 2007.", "T. Yun L. Guan, \"Automatic face detection in video sequences using\nlocal normalization and optimal adaptive correlation techniques\", Patten\nRecognition, pp. 1859-1868, Sep 2009", "M. Valstar and M. Pantic, \"Fully Automatic Facial Action Unit\nDetection and Temporal Analysis\", IEEE Int'l Conf. on Computer\nVision and Pattern Recognition (CVPR'06)(2006).", "N. Ramanathan and R. Chellappa, \"Modeling age progression in young\nfaces,\" in CVPR -06: Proceedings of the 2006 IEEE Computer Society\nConference on Computer Vision and Pattern Recognition. Washington,\nDC, USA: IEEE Computer Society, 2006, pp. 387-394.", "L.G Farkas, \"Anthropometry of the Head and Face\". Raven Press, New\nYork, 1994.", "Xhang, L., Lenders, P.: \"Knowledge-based Eye Detection for Human\nFace Recognition.\" In: Fourth IEEE International Conference on\nKnowledge-Based Intelligent Engineering Systems and Allied\nTechnologies, Vol. 1(2000) , pp. 117-120, 2000", "Rizon, M., Kawaguchi, T. \"Automatic Eye Detection Using Intensity\nand Edge Information.\" In: Proceedings TENCON, Vol. 2(2000), pp.\n415-420, 2000\n[10] Phimoltares, S., Lursinsap, C., Chamnongthai, \"Locating Essential\nFacial Features Using Neural Visual Model.\" In: First International\nConference on Machine Learning and Cybernetics pp. 1914-1919,2002\n[11] Spors, S., Rebenstein, \"A Real-time Face Tracker for Color Video.\" In:\nIEEE International Conference on Acoustics, Speech and Signal\nProcessing, Vol. 3 (2001) 1493-1496\n[12] Perez, C. A., Palma, A., Holzmann C. A., Pena, \" Face and Eye\nTracking Algorithm Based on Digital Image Processing.\" In: IEEE\nInternational Conference on Systems, Man and Cybernetics, Vol. 2\n(2001) 1178-1183\n[13] Marini, R. \"Subpixellic Eyes Detection.\", In: IEEE International\nConference on Image Analysis and Processing (1999) 496-501\n[14] Chandrasekaran, V., Liu, Z. Q. \"Facial Feature Detection Using\nCompact Vector-field Canonical Templates.\" In: IEEE International\nConference on Systems, Man and Cybernetics, Vol. 3 (1997) 2022-\n2027\n[15] Jaimies and N. Sebe, \"Multimodal human computer interaction: A\nsurvey,\" Proceeding of the IEEE International Workshop on Human\nComputer Interaction in conjunction with ICCV, pp.1-15, Beijing,\nChina, October 2005.]\n[16] X. Geng, Z.-H. Zhou, and K. Smith-Miles, \"Automatic age estimation\nbased on facial aging patterns,\" IEEE Transactions on Pattern Analysis\nand Machine Intelligence, vol. 29, no. 12, pp. 2234-2240, 2007\n[17] S. Yan, M. Liu, T. S. Huang, Extracting Age Information from Local\nSpatially Flexible Patches, ICASSP, 2008.\n[18] X. Zhuang, X. Zhou, M. Hasegawa-Johnson, and T. S. Huang, Face\nAge Estimation Using Patch-based Hidden Markov Model\nSupervectors, ICPR, 2008.\n[19] S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, T. S. Huang,\nRegression from Patch-Kernel, ICPR 2008.\n[20] A. Lanitis, Comparative Evaluation of Automatic Age-Progression\nMethodologies, EURASIP Journal on Advances in Signal Processing,\nvolume 8, issue 2, Jan. 2008.\n[21] A. Lanitis, C. J. Taylor, T. F. Cootes, Modeling the process of ageing in\nface images, ICCV, 1999.\n[22] FG-NET Aging Database, http://www.prima.inrialpes.fr/FGnet/, 2002."]}

The evaluation and measurement of human body dimensions are achieved by physical anthropometry. This research was conducted in view of the importance of anthropometric indices of the face in forensic medicine, surgery, and medical imaging. The main goal of this research is to optimization of facial feature point by establishing a mathematical relationship among facial features and used optimize feature points for age classification. Since selected facial feature points are located to the area of mouth, nose, eyes and eyebrow on facial images, all desire facial feature points are extracted accurately. According this proposes method; sixteen Euclidean distances are calculated from the eighteen selected facial feature points vertically as well as horizontally. The mathematical relationships among horizontal and vertical distances are established. Moreover, it is also discovered that distances of the facial feature follows a constant ratio due to age progression. The distances between the specified features points increase with respect the age progression of a human from his or her childhood but the ratio of the distances does not change (d = 1 .618 ) . Finally, according to the proposed mathematical relationship four independent feature distances related to eight feature points are selected from sixteen distances and eighteen feature point-s respectively. These four feature distances are used for classification of age using Support Vector Machine (SVM)-Sequential Minimal Optimization (SMO) algorithm and shown around 96 % accuracy. Experiment result shows the proposed system is effective and accurate for age classification.