Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31824
Optimized Facial Features-based Age Classification

Authors: Md. Zahangir Alom, Mei-Lan Piao, Md. Shariful Islam, Nam Kim, Jae-Hyeung Park


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.

Keywords: 3D Face Model, Face Anthropometrics, Facial Features Extraction, Feature distances, SVM-SMO

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1898


[1] Abu Sayeed Md. Sohail and Prabir Bhattacharya, "Detection of Facial Feature Point Using Anthropometric Face Model", Signal Processing for Image Enhancement and Multimedia Processing, Multimedia System and Application, Volume 31,Part III, 2008.
[2] Udeni Jaysinghe & Anuja Dhrmaratne, "Matching Facial Image using Age Related Morphing Changes", World Academy of Science, Engineering and Technology 06, 2009.
[3] M. Maghami, R. Zoroofi, B. Araabi, M. Shiva and E. Vahedi, "Kalman Filter Tracking for Facial Expression Recognition using Noticeable Feature Selection", ICIAS, pp. 587-590, Nov 2007.
[4] T. Yun L. Guan, "Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques", Patten Recognition, pp. 1859-1868, Sep 2009
[5] M. Valstar and M. Pantic, "Fully Automatic Facial Action Unit Detection and Temporal Analysis", IEEE Int'l Conf. on Computer Vision and Pattern Recognition (CVPR'06)(2006).
[6] N. Ramanathan and R. Chellappa, "Modeling age progression in young faces," in CVPR -06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC, USA: IEEE Computer Society, 2006, pp. 387-394.
[7] L.G Farkas, "Anthropometry of the Head and Face". Raven Press, New York, 1994.
[8] Xhang, L., Lenders, P.: "Knowledge-based Eye Detection for Human Face Recognition." In: Fourth IEEE International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, Vol. 1(2000) , pp. 117-120, 2000
[9] Rizon, M., Kawaguchi, T. "Automatic Eye Detection Using Intensity and Edge Information." In: Proceedings TENCON, Vol. 2(2000), pp. 415-420, 2000
[10] Phimoltares, S., Lursinsap, C., Chamnongthai, "Locating Essential Facial Features Using Neural Visual Model." In: First International Conference on Machine Learning and Cybernetics pp. 1914-1919,2002
[11] Spors, S., Rebenstein, "A Real-time Face Tracker for Color Video." In: IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 3 (2001) 1493-1496
[12] Perez, C. A., Palma, A., Holzmann C. A., Pena, " Face and Eye Tracking Algorithm Based on Digital Image Processing." In: IEEE International Conference on Systems, Man and Cybernetics, Vol. 2 (2001) 1178-1183
[13] Marini, R. "Subpixellic Eyes Detection.", In: IEEE International Conference on Image Analysis and Processing (1999) 496-501
[14] Chandrasekaran, V., Liu, Z. Q. "Facial Feature Detection Using Compact Vector-field Canonical Templates." In: IEEE International Conference on Systems, Man and Cybernetics, Vol. 3 (1997) 2022- 2027
[15] Jaimies and N. Sebe, "Multimodal human computer interaction: A survey," Proceeding of the IEEE International Workshop on Human Computer Interaction in conjunction with ICCV, pp.1-15, Beijing, China, October 2005.]
[16] X. Geng, Z.-H. Zhou, and K. Smith-Miles, "Automatic age estimation based on facial aging patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2234-2240, 2007
[17] S. Yan, M. Liu, T. S. Huang, Extracting Age Information from Local Spatially Flexible Patches, ICASSP, 2008.
[18] X. Zhuang, X. Zhou, M. Hasegawa-Johnson, and T. S. Huang, Face Age Estimation Using Patch-based Hidden Markov Model Supervectors, ICPR, 2008.
[19] S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, T. S. Huang, Regression from Patch-Kernel, ICPR 2008.
[20] A. Lanitis, Comparative Evaluation of Automatic Age-Progression Methodologies, EURASIP Journal on Advances in Signal Processing, volume 8, issue 2, Jan. 2008.
[21] A. Lanitis, C. J. Taylor, T. F. Cootes, Modeling the process of ageing in face images, ICCV, 1999.
[22] FG-NET Aging Database,, 2002.