Abstract
Biometrics is the science of establishing human identity based on the physical or behavioral traits of an individual such as face, iris, ear, hand geometry, finger print, gait, knuckle joints and conjunctival vasculature among others. The enormous attention drawn towards the ocular biometrics during the recent years has led to the exploration of newer traits such as the periocular region. With the preliminary exploration of the feasibility of periocular region to be used as an independent biometric trait or in combination of face/iris, research towards periocular region is currently gaining lot of prominence. Over the last few years many researchers have investigated various techniques of feature extraction and classification in the periocular region. This paper attempts to review a few of these classifier techniques useful for developing robust classification algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Park, U., Jillela, Ross, Jain, A.K.: Periocular Biometrics in the Visible Spectrum. IEEE Transactions on Information Forensics and Security 6(1) (March 2011)
Park, U., Ross, A., Jain, A.K.: Periocular biometrics in the visibility spectrum: A feasibility study. In: Proc. Biometrics: Theory, Applications and Systems (BTAS), pp. 153–158 (2009)
Lyle, J.R., Miller, P., Pundlik, S., Woodard, D.: Soft Biometric Classification using Periocular Region Features. IEEE Transactions (2010)
Distinctive Image Features from Scale- Invariant Keypoints
Hollingsworth, K., Bowyer, K.W., Flynn, P.J.: Identifying useful features for recognition in Near Infrared Periocular Images. IEEE Transactions (2010)
Zhao, W., Arvindh, Rama, Daniel, John: Discriminant Analysis of Principal components for face recognition
Koray, Volkan: PCA for gender estimation: Which Eigenvectors contribute? In: ICPR 2002 (2002)
Aleix, M.: Avinash: PCA versus LDA. IEEE Transactions on PAMI (2001)
Seung, S.: Multilayer perceptrons and backpropogation learning (2002)
Jerome, Trevor, Robert: Additive Logistic Regression: A statistical view of boosting. The Annals of Statistics (2000)
Antonio, Kevin, William: Sharing features: efficient boosting procedures for multiclass object detection
Zhuowen: Probabilistic Boosting Tree: Learning Discriminative Models for Classification. Recognition and Clustering
Corinna, Vladimir: Support Vector Networks. Machine Learning (1995)
Woodard, D., Pundlik, S., Miller, P., Lyle, J.R.: Appearance-based periocular features in the context of face and non-ideal iris recognition. Springer
Merkow, J., Jou, B., Savvides, M.: An Exploration of Gender Identification using only the periocular region. IEEE Transactions (2010)
Merkow, J., Jou, B., Savvides, M.: An Exploration of Gender Identification using only the periocular region. IEEE Transactions (2010)
Woodard, D., Pundlik, S., Lyle, J.R., Miller, P.: Periocular Region AppearanceCues for Biometric Identification. IEEE Transactions (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ambika, D.R., Radhika, K.R., Seshachalam, D. (2012). Periocular Region Classifiers. In: Das, V.V., Stephen, J. (eds) Advances in Communication, Network, and Computing. CNC 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35615-5_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-35615-5_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35614-8
Online ISBN: 978-3-642-35615-5
eBook Packages: Computer ScienceComputer Science (R0)