Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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• A new approach for multimodal biometry has been proposed based on a deci-<br />
sion fusion technique to combine decisions from face and fingerprint classifiers.<br />
This process <strong>of</strong> decision fusion exploits the individual classifier space on the<br />
basis <strong>of</strong> availability <strong>of</strong> class specific information present in the classifier output<br />
space. The class specific contextual information is exploited using LDA and<br />
nonparametric LDA at the classifier response level to enhance class separabil-<br />
ity. Eventhough, face classifier is observed to provide contextual information,<br />
fingerprint classifier <strong>of</strong>ten does not provide this information due to high sensi-<br />
tivity <strong>of</strong> available minutiae points, producing partial matches across subjects.<br />
The enhanced face and fingerprint classifiers are combined using sum rule.<br />
We also propose a generalized algorithm for Multiple Classifier Combination<br />
(MCC) based on our approach. Experimental results exhibit the superiority <strong>of</strong><br />
the proposed method over other existing fusion techniques like sum, product,<br />
max, min rules, decision template and Dempster-Shafer theory.<br />
1.4 Overview <strong>of</strong> the <strong>Thesis</strong><br />
In this thesis, the problem <strong>of</strong> face recognition has been attempted using three differ-<br />
ent techniques and then face and fingerprint information are combined to construct a<br />
multimodal biometric system. The rest <strong>of</strong> the thesis is organized in the following way.<br />
Chapter 2: Literature Review - This chapter discusses the techniques and recent<br />
developments in the field <strong>of</strong> face, fingerprint, and multiple classifier combination. A<br />
few methods based on linear transformation for face recognition are also described<br />
in this chapter. We also provide a vivid description <strong>of</strong> a set <strong>of</strong> methods for Multiple<br />
Classifier Combination (MCC).<br />
Chapter 3: An Efficient Method <strong>of</strong> Face Recognition using Subject-Specific<br />
Subband Faces - This chapter emphasizes on our proposed method <strong>of</strong> face recogni-<br />
tion using Subband face representation. We propose four criteria and an algorithm for<br />
obtaining subject-specific subband faces and then integrate them separately with five<br />
linear subspace methods, namely, PCA, LDA, 2D-PCA, 2D-LDA and DCV. Results<br />
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