Robust authentication using the unconstrained infrared face images

• Information set considers both membership value and pixel intensity together.
• New features are derived on the basis of information sets and s-norm is used to get interactive features.
• Two classifies are developed for robust authentication.
• Promising results are achieved on both the constrained and the unconstrained IR Face database.

Face recognition under the unconstrained conditions that exist in surveillance is the need of the present times. Thus for high end security the research on IR based face recognition assumes importance because of its insensitivity to illumination, disguise and surgery. This paper presents IR face based biometric authentication using the information-set based four types of interactive features and two classifiers. The information sets originate from a fuzzy set on representing the uncertainty associated with the information source instead of a membership function which gives only the degree of association to the fuzzy set. The four feature types include the effective exponential information source (EEI), the effective Gaussian information source (EGI), the effective multi quadratic information source (EMQDI) and inverse of this feature (EIMQDI). The interactive features are obtained by taking the s-norms on the features from the successive windows. Two classifiers called the Hanman Classifier and the weighted Hanman Classifier are formulated using t-norms. The features and classifiers are tested on the created databases incorporating the unconstrained conditions such as occlusion, less resolution and noise.