Lucia Cariello

A face recognition system based on Pseudo 2D HMM applied to neural network coefficients

Face recognition from an image or video sequences is emerging as an active research area with numerous commercial and law enforcement applications. In this paper different Pseudo 2-dimension Hidden Markov Models (HMMs) are introduced for a face recognition showing performances reasonably fast for binary images. The proposed P2-D HMMs are made up of five levels of states, one for each significant facial region in which the input frontal images are sequenced: forehead, eyes, nose, mouth and chin. Each of P2-D HMMs has been trained by coefficients of an artificial neural network used to compress a bitmap image in order to represent it with a number of coefficients that is smaller than the total number of pixels. All the P2-D HMMs, applied to the input set consisting of the Olivetti Research Laboratory face database combined to others photos, have achieved good rates of recognition and, in particular, the structure 3-6-6-6-3 has achieved a rate of recognition equal to 100%.

Retinal Fundus Biometric Analysis for Personal Identifications

In this paper a biometric system for personal identification, realized through the manipulation of retinal fundus images and the detection of its bifurcation points, is described. In the image pre-processing step, a strong contrast exaltation between blood vessels and the background in retinal image is carried out; then blood vessels are extracted and next the vasculature bifurcation and crossover points are identified within squared shaped regions used to window the image. Finally the features sets are compared with a pattern recognition algorithm and a novel formulation is introduced to evaluate a similarity score and to obtain the personal identification.

Retinal vessel extraction by a combined neural network-wavelet enhancement method

This paper presents a combined approach to automatic extraction of blood vessels in retinal images. The proposed procedure is composed of two phases: a wavelet transform-based preprocessing phase and a NN-based one. Several neural net topologies and training algorithms are considered with the aim of selecting an effective combined method. Human retinal fundus images, derived from the publicly available ophthalmic database DRIVE, are processed to detect retinal vessels. The approach is tested by considering performances in terms of sensitivity and specificity values obtained from vessel classification. The quality of vessel identifications is evaluated on obtained image by computing both sensitivity values and specificity ones and by relating them in ROC curves. A comparison of performances by ROC curve areas for various methods is reported.

Pseudo 2D Hidden Markov Models for Face Recognition Using Neural Network Coefficients

Face recognition is the preferred mode of identity recognition by humans from an image or video sequence: it is natural, robust and unintrusive. This work presents different pseudo 2D HMM structures for a face recognition showing performances reasonably fast for binary image. The proposed P2-D HMMs are made up of five levels of states, one for each region of interest (Rol) in which the input frontal images are sequenced: forehead, eyes, nose, mouth and chin. Each of P2-D HMMs has been trained by coefficients of an artificial neural network used to compress a bitmap image in order to represent it with a number of coefficients that is smaller than the total number of pixels. All the P2-D HMMs, applied to the validation set consisting of the Olivetti Research Laboratory (ORL) face database, have achieved good rates of recognition compared to other methods proposed in the literature and, in particular, the structure 3-6-6-6-3 has achieved a rate of recognition equal to 100%.

Defects Identification in Textile by Means of Artificial Neural Networks

In this paper we use a neural network approach for defects identification in textile. The images analyzed came from an artificial vision system that we used to acquire and memorize them in bitmap file format. The vision system is made of two grey scale line scan camera arrays and each array is composed of four CCD cameras with a sensor of 2048 pixels. Every single camera has a field of view of 600mm. The big amount of pixels to be studied to determine whether the texture is defective or not, requires the implementation of some encoding technique to reduce the number of the significant elements. The artificial neural networks (ANN) are manipulated to compress a bitmap that may contain several defects in order to represent it with a number of coefficients that is smaller than the total number of pixel but still enough to identify all kinds of defects classified. An error back propagation algorithm is also used to train the neural network. The proposed technique includes, also, steps to break down large images into smaller windows or array and eliminate redundant information.

Biomedical Text Mining Using a Grid Computing Approach

Extracting useful information from a very large amount of biomedical texts is an important and difficult activity in biomedicine field. Data to be examined are generally unstructured and the available computational resources do not still provide adequate mechanisms for retrieving and analyse very large amount of contents. In this paper we present a rule-based system for Text Mining process applied in biomedical textual documents. This application requires a strongly use of the computational resource to perform intensive operations. We propose a grid computing approach to improve application performance.