• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Face Recognition Invariant to Partial Occlusions


Abstract

Face recognition is considered a complex biometrics in the field of image processing mainly due to the constraints imposed by variation in the appearance of facial images. These variations in appearance are affected by differences in expressions and/or occlusions (sunglasses, scarf etc.). This paper discusses incremental Kernel Fisher Discriminate Analysis on sub-classes for dealing with partial occlusions and variant expressions. This framework focuses on the division of classes into fixed size sub-classes for effective feature extraction. For this purpose, it modifies the traditional Linear Discriminant Analysis into incremental approach in the kernel space. Experiments are performed on AR, ORL, Yale B and MIT-CBCL face databases. The results show a significant improvement in face recognition.


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Cite this article

[IEEE Style]
A. Aisha, S. Muhammad, S. J. Hussain and R. Mudassar, "Face Recognition Invariant to Partial Occlusions," KSII Transactions on Internet and Information Systems, vol. 8, no. 7, pp. 2496-2511, 2014. DOI: 10.3837/tiis.2014.07.017.

[ACM Style]
Azeem Aisha, Sharif Muhammad, Shah Jamal Hussain, and Raza Mudassar. 2014. Face Recognition Invariant to Partial Occlusions. KSII Transactions on Internet and Information Systems, 8, 7, (2014), 2496-2511. DOI: 10.3837/tiis.2014.07.017.