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The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machi ...
Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...
The performance of face verification systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machine ...
This paper presents a general method for incorporating prior knowledge into kernel methods such as Support Vector Machines. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assumi ...
The present paper describes a new method for manufg. a nanostructured porous layer of TiO2 on a conducting glass substrate for use in a dye-sensitized photoelectrochem. cell. The method involves deposition of a layer of semiconductor particles onto a condu ...
The Circle Hough Transform (CHT) has become a common method for circle detection in numerous image processing applications. Because of its drawbacks, various modifications to the basic CHT method have been suggested. This paper presents an algorithm to fin ...
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 ei ...
We 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, genera ...
Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithm ...
Detecting faces in images is a key step in numerous computer vision applications as face recognition for example. Face detection is a difficult task in image analysis because of the large face intra-class variability which is due to the important influence ...
