We address the problem of unconstrained face recognition from remotely acquired images.
The main factors that make this problem challenging are image degradation due to blur, and …

Recent research of sparse signal representation has aimed at learning discriminative sparse
models instead of purely reconstructive ones for classification tasks, such as sparse …

Biometric cryptosystems combine biometrics with cryptography by producing Biometric
Cryptographic Keys (BCKs) to provide stronger security mechanisms while protecting …

To deal with severe variation in recording conditions, most biometric systems acquire
multiple biometric samples, at the enrolment stage, for the same person and then extract …

Face recognition has the wide research and applications on many areas. Many surveys of
face recognition are implemented. Different from previous surveys on from a single …

Relative Entropy (RE) of individual's biometric features is the amount of information that
distinguishes the individual from a given population. This paper presents an analysis of RE …

Existing face recognition schemes are mostly based on extracting biometric feature vectors
either from whole face images, or from a fixed facial region (eg, eyes, nose, and mouth) …

Since modeling reflections in image processing is a difficult task, most computer vision
algorithms assume that objects are Lambertian and that no lighting change occurs. Some …

A pattern recognition system compares a set of unlabeled images (or other patterns) having
a variation of state in a set-by-set comparison with individual data sets of multiple labeled …

In this research paper the three very important illumination normalization techniques
discussed specifically Log About, Homomorphic filter and Wavelets for effective face …