Gabriela E. Martinez

Modular Neural Networks and Fuzzy Sugeno Integral for Face and Fingerprint Recognition

We describe in this paper a new approach for pattern recognition using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human faces and fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral with some modifications. Response integration is required to combine the outputs of all the modules in the modular network. We have applied the new approach for fingerprint and face recognition with a real database from students of our institution.

Modular Neural Networks and Fuzzy Sugeno Integral for Pattern Recognition: The Case of Human Face and Fingerprint

We describe in this paper a new approach for pattern recogni- tion using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human faces and fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral with some modifications. Response integra- tion is required to combine the outputs of all the modules in the modular network. We have applied the new approach for fingerprint and face rec- ognition with a real database from students of our institution.

Optimization of modular neural networks using hierarchical genetic algorithms applied to speech recognition

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms. Modular neural networks (MNN) have shown significant learning improvement over single neural networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a hierarchical genetic algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results shown in this paper prove the feasibility and advantages of the proposed approach. The method of integration of response is based on fuzzy integral and Sugeno measures, where parameter /spl lambda/ also is optimized by means of the hierarchical genetic algorithms.

An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

In this work an extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM) algorithm to observe if the proposed approach has better performance than this algorithm.

Face Recognition with Choquet Integral in Modular Neural Networks

In this chapter a new method for response integration, based on Choquet Integral is presented. A type-1 fuzzy system for edge detections based in Sobel and Morphological gradient is used, which is a pre-processing applied to the training data for better performance in the modular neural network. The Choquet integral is used how method to integrate the outputs of the modules of the modular neural networks (MNN). A database of faces was used to perform the pre-processing, the training, and the combination of information sources of the MNN.

Review of Recent Type-2 Fuzzy Image Processing Applications

This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

Face Recognition with a Sobel Edge Detector and the Choquet Integral as Integration Method in a Modular Neural Networks

In this paper a method for response integration of Modular Neural Networks, based on Choquet Integral applied to face recognition is presented. Type-1 and Type-2 fuzzy systems for edge detections based on the Sobel, which is a pre-processing applied to the training data for better performance in the modular neural network. The Choquet integral is an aggregation operator that in this case is used as a method to integrate the outputs of the modules of the modular neural networks (MNN).

A new approach based on generalized type-2 fuzzy logic for edge detection

This paper presents an edge detection method based on morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic. For the test we used the method of defuzzification by height and approximation. The simulation results were obtained with a type-1 fuzzy inference system (T1FIS), an interval type-2 fuzzy inference system (IT2FIS) and with a generalized type-2 fuzzy logic (GT2FIS). The proposed type-2 fuzzy edge detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of the use of generalized type-2 fuzzy logic.

Choquet Integral and Interval Type-2 Fuzzy Choquet Integral for Edge Detection

In this paper, a method for edge detection in digital images based on morphological gradient technique in combination with Choquet integral, and the interval type-2 Choquet integral is proposed. The aggregation operator is used as a method to integrate the four gradients of the edge detector. Simulation results with real images and synthetic images are presented and the results show that the interval type-2 Choquet integral is able to improve the detected edge.

Generalized type-2 fuzzy logic in response integration of modular neural networks

In this paper a new method for response integration, based on generalized type-2 fuzzy logic, in modular neural networks (MNNs) is presented. The main idea is that the uncertainty in combining the outputs of the different modules in the MNN can be handled in a better way by using type-2 fuzzy logic. Previous works have considered using interval type-2 fuzzy logic for this task, but in this paper we are proposing the use of generalized type-2 fuzzy logic to improve the overall results of MNNs. The new method was tested with the problem of face recognition, showing that generalized type-2 fuzzy logic outperforms other approaches for the same task.