The Central Tendency, Weighted Likelihood, and Exponential family

In this paper, we establish the links between the H\"older and Lehmer central tendencies and the maximum likelihood for the estimation of the one-parameter exponential family of probability density functions. For this, we show that the maximum weighted likelihood of the parameter is a generalized weighted mean from which the central tendencies of H\"older and Lehmer can be inferred. Some of the links obtained do not seem to be part of the state of the art. Moreover, we show that the maximum weighted likelihood is equivalent to the minimum of the weighted least square error. Experimentations confirm that the maximum weighted likelihood leads to a more accurate fitting of histograms

Blind Image Quality Assessment for Face Pose Problem

No-Reference image quality assessment for face images is of high interest since it can be required for biometric systems such as biometric passport applications to increase system performance. This can be achieved by controlling the quality of biometric sample images during enrollment. This paper proposes a novel no-reference image quality assessment method that extracts several image features and uses data mining techniques for detecting the pose variation problem in facial images. Using subsets from three public 2D face databases PUT, ENSIB, and AR, the experimental results recorded a promising accuracy of 97.06% when using the RandomForest Classifier, which outperforms other classifier