Faceparts for recognition

A facial classification system that utilises images of faceparts is presented in this paper. Each facepart region is allocated a degree of importance. The random forests approach is employed for classification. The approach grows many classification trees where each tree gives a classification decision. The forest selects the classification that gives the most votes. Experimental results are presented and discussed <br /

Facial expression synthesis

A new method is presented in this paper for synthesis of an expressionless face image from a face image containing an arbitrary known expression. The proposed method enhances the performance of the existing principal components analysis which can be trained using a limited set of expressionless face images. This performance enhancement is achieved through the extraction of the basis set of the example face images in a global-local-based fashion

Illumination-effects compensation in facial images

Based on the concepts of linear object classes and the principal components analysis, an illumination-effects compensation method is presented to transform an arbitrary-lit face image whose illumination effects are pre-determined, into a front-lit face image

Quadtree principal component analysis and its application to facial expression classification

This paper presents a method called quadtree principal components analysis for facial expression classification. The quadtree principal components analysis is an image transformation that takes its name from the quadtree partition scheme on which it is based. The quadtree principal components analysis method implements a global-local decomposition of the input face image. This solves the problems associated with the existing principal components analysis and local principal components analysis methods when applied to facial expression classification

Road-sign identification using ensemble learning

Ensemble learning that combines the decisions of multiple weak classifiers to from an output, has recently emerged as an effective identification method. This paper presents a road-sign identification system based upon the ensemble learning approach. The system identifies the regions of interest that are extracted from the scene into the road-sign groups that they belong to. A large road-sign image dataset is formed and used to train and test the system. Fifteen groups of road signs are chosen for identification. Five experiments are performed and the results are presented and discussed.<br /

Commonsense knowledge representation and reasoning with fuzzy neural networks

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is employed as a basic element for the connectionist model. The representation and reasoning ability of the model is described through examples

Face image matching using fractal dimension

A new method is presented in this paper for calculating the correspondence between two face images on a pixel by pixel basis. The concept of fractal dimension is used to develop the proposed non-parametric area-based image matching method which achieves a higher proportion of matched pixels for face images than some well-known methods

Cancer detective

The artile describes development of an automated system for detection of lung nodules in CT images

Multilabel classification by BCH code and random forests

This paper uses error correcting codes for multilabel classification. BCH code and random forests learner are used to form the proposed method. Thus, the advantage of the error-correcting properties of BCH is merged with the good performance of the random forests learner to enhance the multilabel classification results. Three experiments are conducted on three common benchmark datasets. The results are compared against those of several exiting approaches. The proposed method does well against its counterparts for the three datasets of varying characteristics.<br /