In this paper, we propose a simple and efficient face representation feature that adopts the eigenfaces of Local Binary Pattern (LBP) space, referred to as the LBP eigenfaces, for robust face recognition. In the proposed method, LBP eigenfaces are generated by first mapping the original image space to the LBP space and then projecting the LBP space to the LBP eigenface subspace by Principal Com...

A strategy of color image based human face representation is first proposed. Then, based on this representation, complex Eigenfaces technique is developed for facial feature extraction. Finally, we test our idea using the AR face database. The experimental result demonstrates that the proposed color image based complex Eigenfaces method is more robust to illumination variations than the traditi...

In this paper we discuss some problem of Eigenfaces which is ignores in the previous work that is the question of which features are important for classification, and which are not. Eigenfaces seeks to answer this by using principal component analysis of the images of the faces. This analysis reduces the dimensionality of the training set, leaving only those features that are critical for face ...

We 2 present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Another issue addressed is how to select the number of eigenfaces to achieve a good classification rate. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies and discuss their effectiveness in the case of ...

Abstract Effective monitoring of the tool wear condition within a machining process can be very challenging. Depending on sensors used, often only part relevant information detected. In case milling processes data acquisition is made even more difficult by fact that working point inaccessible for sensor applications due to physical tool, itself, chipping and used cooling-lubricants. By using va...

1 The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, gener...