Claudio M. Held

Subscription fraud prevention in telecommunications using fuzzy rules and neural networks

A system to prevent subscription fraud in fixed telecommunications with high impact on long-distance carriers is proposed. The system consists of a classification module and a prediction module. The classification module classifies subscribers according to their previous historical behavior into four different categories: subscription fraudulent, otherwise fraudulent, insolvent and normal. The prediction module allows us to identify potential fraudulent customers at the time of subscription. The classification module was implemented using fuzzy rules. It was applied to a database containing information of over 10,000 real subscribers of a major telecom company in Chile. In this database a subscription fraud prevalence of 2.2% was found. The prediction module was implemented as a multilayer perceptron neural network. It was able to identify 56.2% of the true fraudsters, screening only 3.5% of all the subscribers in the test set. This study shows the feasibility of significantly preventing subscription fraud in telecommunications by analyzing the application information and the customer antecedents at the time of application.

Extracting Fuzzy Rules From Polysomnographic Recordings for Infant Sleep Classification

A neuro-fuzzy classifier (NFC) of sleep-wake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20-s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, REM-Sleep, Non-REM Sleep Stage 1, Stage 2, and Stage 3-4. The definite criterion for a sleep state or stage to be established is duration of at least 1 min. The data set consisted of a total of 14 continuous recordings of naturally occurring naps (average duration: 143plusmn39 min), corresponding to a total of 6021 epochs. They were divided in a training, a validation and a test set with 7, 2, and 5 recordings, respectively. During supervised training, the system determined the fuzzy concepts associated to the inputs and the rules required for performing the classification, extracting knowledge from the training set, and pruning nonrelevant rules. Results on an independent test set achieved 83.9plusmn0.4% of expert agreement. The fuzzy rules obtained from the training examples without a priori information showed a high level of coincidence with the crisp rules stated by the experts, which are based on internationally accepted criteria. These results show that the NFC can be a valuable tool for implementing an automated sleep-wake classification system

Automated Sleep-Spindle Detection in Healthy Children Polysomnograms

We present a new methodology to detect and characterize sleep spindles (SSs), based on the nonlinear algorithms, empirical-mode decomposition, and Hilbert-Huang transform, which provide adequate temporal and frequency resolutions in the electroencephalographic analysis. In addition, the application of fuzzy logic allows to emulate experts procedures. Additionally, we built a database of 56 all-night polysomnographic recordings from children for training and testing, which is among the largest annotated databases published on the subject. The database was split into training (27 recordings), validation (10 recordings), and testing (19 recordings) datasets. The SS events were marked by sleep experts using visual inspection, and these marks were used as golden standard. The overall SS detection performance on the testing dataset of continuous all-night sleep recordings was 88.2% sensitivity, 89.7% specificity, and 11.9% false-positive (FP) rate. Considering only non-REM sleep stage 2, the results showed 92.2% sensitivity, 90.1% specificity, and 8.9% FP rate. In general, our system presents enhanced results when compared with most systems found in the literature, thus improving SS detection precision significantly without the need of hypnogram information.

Face and iris localization using templates designed by particle swarm optimization

Face and iris localization is one of the most active research areas in image understanding for new applications in security and theft prevention, as well as in the development of human-machine interfaces. In the past, several methods for real-time face localization have been developed using face anthropometric templates which include face features such as eyes, eyebrows, nose and mouth. It has been shown that accuracy in face and iris localization is crucial to face recognition algorithms. An error of a few pixels in face or iris localization will produce significant reduction in face recognition rates. In this paper, we present a new method based on particle swarm optimization (PSO) to generate templates for frontal face localization in real time. The PSO templates were tested for face localization on the Yale B Face Database and compared to other methods based on anthropometric templates and Adaboost. Additionally, the PSO templates were compared in iris localization to a method using combined binary edge and intensity information in two subsets of the AR face database, and to a method based on SVM classifiers in a subset of the FERET database. Results show that the PSO templates exhibit better spatial selectivity for frontal faces resulting in a better performance in face localization and face size estimation. Correct face localization reached a rate of 97.4% on Yale B which was higher than 96.2% obtained with the anthropometric templates and much better than 60.5% obtained with the Adaboost face detection method. On the AR face subsets, different disparity errors were considered and for the smallest error, a 100% correct detection was reached in the AR-63 subset and 99.7% was obtained in the AR-564 subset. On the FERET subset a detection rate of 96.6% was achieved using the same criteria. In contrast to the Adaboost method, PSO templates were able to localize faces on high-contrast or poorly illuminated environments. Additionally, in comparison with the anthropometric templates, the PSO templates have fewer pixels, resulting in a 40% reduction in processing time thus making them more appropriate for real-time applications.

Expert-system classification of sleep/waking states in infants

This work is part of a project to develop an expert system for automated classification of the sleep/waking states in human infants; i.e. active or rapid-eye-movement sleep (REM), quiet or non-REM sleep (NREM), including its four stages, indeterminate sleep (IS) and wakefulness (WA). A model to identify these states, introducing an objective formalisation in terms of the state variables characterising the recorded patterns, is presented. The following digitally recorded physiological events are taken into account to classify the sleep/waking states: predominant background activity and the existence of sleep spindles in the electro-encephalogram; existence of rapid eye movements in the electro-oculogram; and chin muscle tone in the electromyogram. Methods to detect several of these parameters are described. An expert system based on artificial ganglionar lattices is used to classify the sleep/waking states, on an off-line minute-by-minute basis. Algorithms to detect patterns automatically and an expert system to recognise sleep/waking states are introduced, and several adjustments and tests using various real patients are carried out. Results show an overall performance of 96.4% agreement with the expert on validation data without artefacts, and 84.9% agreement on validation data with artefacts. Moreover, results show a significant improvement in the classification agreement due to the application of the expert system, and a discussion is carried out to justify the difficulties of matching the experts criteria for the interpretation of characterising patterns.

Gender Classification From Face Images Using Mutual Information and Feature Fusion

In this article we report a new method for gender classification from frontal face images using feature selection based on mutual information and fusion of features extracted from intensity, shape, texture, and from three different spatial scales. We compare the results of three different mutual information measures: minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), and conditional mutual information feature selection (CMIFS). We also show that by fusing features extracted from six different methods we significantly improve the gender classification results relative to those previously published, yielding 99.13% of the gender classification rate on the FERET database.

Real-Time Template Based Face and Iris Detection on Rotated Faces

Real-time iris and face detection on video sequences is important in applications such as study of the eye function, drowsiness detection, man-machine interfaces, face recognition security and multimedia retrieval. In this work, we present a real-time template based method for iris detection in faces with wide coronal (− 40°, + 40°) and transversal (− 45°, + 45°) axis rotations. This method is based on anthropometric templates that were constructed off-line for face coronal and transversal rotation, using face features such as elliptical shape, location of the eyebrows, nose and lips. A line integral is computed using these templates over the fine directional image to find the actual face location, face size and rotation angle. This information provides a region to search for the eyes and the iris boundary is detected. Results computed on five video sequences including coronal and transversal rotations with over 1,700 frames show correct face detection rate of 98.5% and iris detection rate of 94.4%. The method was compared with a “weighting mask method” on two video sequences showing an improved performance. The method was also compared for eye detection to a method using combined binary edge and intensity information in two subsets of the AR face database (63 and 564 images). Different disparity errors were considered and for the smallest error, a 100% correct detection was reached in the AR-63 subset and 99.8% was obtained in the AR-564 subset.

Classification of sleep stages in infants: a neuro fuzzy approach

An ANFIS based neuro-fuzzy system to classify sleep-waking states and stages in healthy infants has been developed. The classifier takes rive input patterns identified from polysomnographic recordings on 20 s frames and assigns them to one out of rive possible classes (WA, NREM-I, NREM-II, NREM-III&IV or REM). Eight polysomnographic recordings of healthy infants were studied, making a total of 3510 frames. Of these, four recordings were used for training, two for validation and two for testing. Results on the testing data achieved on average 88.2% of expert agreement in sleep-waking state-stage classification. These results were compared with the ones obtained using a multi-layer perceptron neural network (87.3%) and by applying the experts rules for sleep classification (86.7%). The neuro-fuzzy approach also rendered fuzzy classification rules, which were analyzed and compared with the experts rules.

Dual approach for automated sleep spindles detection within EEG background activity in infant polysomnograms

An automated system for sleep spindles detection within EEG background activity, combining two different approaches, is presented. The first approach applies detection criteria on the sigma-band filtered EEG signal, including fuzzy thresholds. The second approach mimics an experts procedure. A sleep spindle detection is validated if both approaches agree. The method was applied on a testing set, consisting of continuous sleep recordings of two patients, totaling 1132 epochs (pages). A total of 803 sleep spindles events were marked by the experts. Results showed an 87.7% agreement between the detection system and the medical experts.

Design of a virtual keyboard based on iris tracking

The development of man-machine interfaces to control devices with the eyes could be of great impact in handicapped individuals. In this paper a non-obstructive interface is proposed to detect and track iris position based on digital image processing techniques, templates and a reference point mounted near the eye. The position of the iris is detected in four steps: reference detection, iris center detection, iris position computation relative to the reference and determination of the eye position within the virtual keyboard. The position of the iris is projected over a virtual keyboard and it is determined the maximum number of keys that the system is able to discriminate. The percentage of iris correct detection in four different video sequences of 803, 710, 913, 849 frames, respectively were above 98.2%. The reference detection was 100% correct. A virtual keyboard was built allowing 12 horizontal and 8 vertical keys.