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This paper introduces an Automatic Target Recognition (ATR) method based on X Band Radar image processing. A software which implements this method was developed following four principal stages: digital image formation, image preprocessing, feature selection through a combination of C4.5 Decision Tree and PCA and classification using SVM. The automatic process was validated using two images sets, one of them containing real images with natural noise levels and the other with different degrees of impulsive noise contamination. The method achieves a very nice computation behavior and effectiveness, high accuracy and robustness in noise environments with a low storage memory and high decision speed.

The iris localization plays a fundamental role in the recognition process because the speed and performance of the iris recognition system largely depends on the quality of the pupil and iris detection. This process includes the detection of inner (pupil) and outer (iris) boundaries. In this paper we present a new method for iris and pupil boundaries detection based on Adaboosting technique for localization of circular objects and an algorithm based on the elements of analytic geometry, in particular, the determination of the bounded circumference of a tangential square that encloses the pupil and iris. The proposed approach overcomes the limitations that had previous methods regarding the use of images obtained under not controlled conditions like specular light reflected in the pupil or in the iris. We experimented our approach comparing the results in detection with the results obtained by Daugman algorithm using images from two contrasting databases, CASIA and UBIRIS.

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In this paper we present a method for the automatic localization of local light variations and its photometric normalization in face images affected by different angles of illumination causing the appearance of specular light. The proposed approach is faster and more efficient that if the same one was carried out on the whole image through the traditional photometric normalization methods (homomorphic filtering, anisotropic smoothing, etc.). The process consists in using of the Adaboosting algorithms for the fast detection of regions affected by specular reflection combined with a normalization method based on the local normalization that standardizes the local mean and variance into the located region. A set of measures are proposed to evaluate the effectiveness of detectors. Finally, results are compared through two experimental schemes to measure how the similarity is affected by illumination changes and how the proposed approach improves the effect caused by these changes.