Binarized eigenphases applied to limited memory face recognition systems
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Most of the algorithms proposed for face recognition involve considerable amount of computations and hence they cannot be used on devices constrained with limited memory. In this paper, we propose a novel solution for efficient face recognition problem for the systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. The binarization step that is applied to the phases adds many interesting advantages to the system. It will be shown that the proposed technique increases the face recognition rate and at the same time achieves substantial savings in the computational time, when compared to other known systems. Experiments on two independent databases of face images are reported to demonstrate the effectiveness of the proposed technique.
KeywordsFace recognition Limited memory PCA MPEG-7
The authors would like to thank the reviewers for the constructive suggestions and valuable comments.
- 6.Cai J, Goshtasby A (1999) Detecting human faces in color images. Image Vis Comput 63–75Google Scholar
- 8.Ng C, Savvides M, Khosla PK (2005) Real-time face verification system on a cell-phone using advanced correlation filters. Automatic identification advanced technologies, fourth IEEE workshop on 57–62, New York, USAGoogle Scholar
- 9.Zaeri N, Mokhtarian F, Cherri A (2006) Fast face recognition techniques for small and portable devices. Proceedings of the IEEE, ItalyGoogle Scholar
- 13.Kim HC, Kim D, Bang SY (2002) Face retrieval using 1st- and 2nd-order PCA mixture model. International conference on image processing, Rochester, NYGoogle Scholar
- 14.Kong H, Li X, Wang J-G, Teoh EK, Kambhamettu C (2005) Discriminant low-dimensional subspace analysis for face recognition with small number of training samples. British machine vision conference (BMVC), Oxford, UK, 5–9 September 2005Google Scholar
- 15.Savvides M, Vijaya Kumar BVK, Khosla PK (2004) Eigenphases vs. eigenfaces. IEEE-17th international conference on pattern recognitionGoogle Scholar
- 17.Lipinski L, Yamada A (2003) MPEG-7 Face recognition technique, international organization for standardization, ISO/IEC JTC1/SC29/WG11, coding of moving pictures and audio, NPEG03/N6188Google Scholar
- 18.Kamei T, Yamada A (2002) Report of core experiment on Fourier spectral PCA based face description, ISO/IEC JTC1/SC21/WG11 M8277, Fairfax, VAGoogle Scholar
- 20.Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) XM2VTSDB: The extended M2VTS database. Second international conference on audio and video-based biometric person authenticationGoogle Scholar
- 21.Phillips P, Wechsler H, Huang J, Rauss P (1998) The FERET database and evaluation procedure for face-recognition algorithms. J Image Vis Comput 295–306Google Scholar
- 22.Moghaddam B, Jebara T, Pentland A (1999) Bayesian modeling of facial similarity. In: Kearns MJ, Solla SA, Cohn DA (eds) Advances in neural information processing system 11. MIT Press, Cambridge, pp 910–916Google Scholar