The main challenge of facial biometrics is its robustness and ability to adapt to changes in position orientation, facial expression, and illumination effects. This research addresses the predominant deficiencies in this regard and systematically investigates a facial authentication system in the Euclidean domain. In the proposed method, Euclidean geometry in 2D vector space is being constructed for features extraction and the authentication method. In particular, each assigned point of the candidates’ biometric features is considered to be a 2D geometrical coordinate in the Euclidean vector space. Algebraic shapes of the extracted candidate features are also computed and compared. The proposed authentication method is being tested on images from the public “Put Face Database”. The performance of the proposed method is evaluated based on Correct Recognition (CRR), False Acceptance (FAR), and False Rejection (FRR) rates. The theoretical foundation of the proposed method along with the experimental results are also presented in this paper. The experimental results demonstrate the effectiveness of the proposed method.
Space Time Adaptive Processing (STAP) is a multi-dimensional adaptive signal processing technique,
which processes the signal in spatial and Doppler domains for which a target detection hypothesis
is to be formed. It is a sample based technique and based on the assumption of adequate number
of Independent and Identically Distributed (i.i.d.) training data set in the surrounding environment.
The principal challenge of the radar processing lies when it violates these underlying assumptions due
to severe dynamic heterogeneous clutter (hot clutter) and jammer effects. This in turn degrades the
Signal to Interference-plus-Noise Ratio (SINR), hence signal detection performance. Classical Wiener
filtering theory is inadequate to deal with nonlinear and nonstationary interferences, however Wiener
filtering approach is optimal for stationary and linear systems. But, these challenges can be overcome
by Adaptive Sequential State Estimation (ASSE) filtering technique.