Abstract
Face recognition has been extensively explored in diversified applications on ubiquitous devices. Most of the research has been primarily focused on full frontal/profile of facial images while proposing novice techniques to pursue this problem. The resource constraint in Mobile devices adds more complexity to the face recognition process. To reduce computational requirements some investigations are made to use the partial faces for recognition process. However, the inadequate information in partial faces makes the problem much more challenging and therefore limited attempts have been made in this direction.
Our Active pixel based approach is capable of recognizing the persons using either full or partial face information. The technique reduces the computational resources compared to the LBP which was claimed as one of the most suitable approach on mobile devices. We have carried out the experiments on the YALE facial databases. Other works [1, 2] have used 50% vertical portion and showed the accuracy of correct recognition 94% within best five matches. In our dynamic partial matching we have used 10% to 34% image and obtained correct recognition rate from 96% to 100% within best three matches.
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
Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19(7), 775-779 (1997)
Elastic and Partial Matching Metric for Face Recognition. IEEE 12th International Conference on Computer Vision, ICCV 2009, pp. 2082-2089 (2009)
Mukherjee, S., et al.: A secure face recognition system for mobile-devices without the need of decryption (2008)
Hull, J.J., et al.: Mobile image recognition: architectures and tradeoffs, pp. 84–88. ACM, New York (2010)
Sen, S., et al.: Exploiting approximate communication for mobile media applications, pp. 1–6. ACM, New York (2009)
Junered, M.s.I.: Face recognition in mobile devices. Luleåtekniska university (2010)
Mallikarjuna Rao, G., Babu, G.R., Vijaya Kumari, G., Krishna Chaitanya, N.: Methodological Approach for Machine based Expression and Gender Classification. In: IEEE Int. Advance Computing Conference, pp. 1369–1374 (2009)
YALE Face Data base, http://www.vision.ucsd.edu
Reitboeck, H., Brody, T.P.: A Transformation With Invariance Under Cyclic Permutation for Applications in Pattern Recognition. Inf. & Control 15(2), 130–154 (1969)
Ojala, T., Pietikǎinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rao, G.M., Kumar, P., Kumari, G.V., Pande, A., Babu, G.R. (2011). An Investigation into the Use of Partial Face in the Mobile Environment. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-24031-7_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24030-0
Online ISBN: 978-3-642-24031-7
eBook Packages: Computer ScienceComputer Science (R0)