Abstract
In this paper, a scheme was presented to identify the locations of key features of a human face such as eyes, nose, chin known as the fiducial points and form a face graph. The relative distances between these features are calculated. These distance measures are considered to be unique identifying attributes of a person. The distance measures are used to train a Support Vector Machine (SVM). The identification takes place by matching the features of the presented person with the features that were used to train the SVM. The closest match results in identification. The Minimum Distance Classifier has been used to recognize a person uniquely using this SVM.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Tie, Y., Guan, L.: Automatic landmark point detection and tracking for human facial expressions. J. Image Video Proc. 2013, 8 (2013). https://doi.org/10.1186/1687-5281-2013-8
Yun, T., Guan, L.: Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques. Pattern Recogn. 42(9), 1859–1868 (2009)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. J. Comput. Surv. (CSUR) 35(4), 399–458 (2003). https://doi.org/10.1145/954339.954342
Nair, P., Cavallaro, A.: 3-D face detection, landmark localization, and registration using a point distribution model. IEEE Trans. Multimed. 11(4), 611–623 (2009). https://doi.org/10.1109/tmm.2009.2017629
Shi, J., Samal, A., Marrx, D.: How effective are landmarks and their geometry for face recognition? Comput. Vis. Image Underst. Elseivier 102(2), 117–133 (2006)
Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. Comput. Vis. Pattern Recogn. (CVPR), IEEE. (2010) https://doi.org/10.1109/cvpr.2010.5539992
Celikutan, O., Ulukaya, S., Sankur, B.: A comparative study of face landmarking techniques. EURASIP J. Image Video Process. (2013). Springer. https://doi.org/10.1186/1687-5281-2013-13
O’Shaughnessy, D.: Automatic speech recognition: history, methods and challenges. Pattern Recogn. 41(10), 2965–2979 (2008). Elseiveier
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. 3rd. ed. Pearson Education (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dastidar, J.G., Basak, P., Hota, S., Athar, A. (2018). SVM Based Method for Identification and Recognition of Faces by Using Feature Distances. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-7566-7_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7565-0
Online ISBN: 978-981-10-7566-7
eBook Packages: EngineeringEngineering (R0)