Vamsi Krishna Madasu

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance.

An Abandoned Object Detection System Based on Dual Background Segmentation

An abandoned object detection system is presented and evaluated using benchmark datasets. The detection is based on a simple mathematical model and works efficiently at QVGA resolution at which most CCTV cameras operate. The pre-processing involves a dual-time background subtraction algorithm which dynamically updates two sets of background, one after a very short interval (less than half a second) and the other after a relatively longer duration. The framework of the proposed algorithm is based on the Approximate Median model. An algorithm for tracking of abandoned objects even under occlusion is also proposed. Results show that the system is robust to variations in lighting conditions and the number of people in the scene. In addition, the system is simple and computationally less intensive as it avoids the use of expensive filters while achieving better detection results.

A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

Fuzzy Model Based Recognition of Handwritten Hindi Numerals using Bacterial Foraging

This paper presents the recognition of handwritten Hindi numerals. The recognition is based on the modified exponential membership function fitted to the fuzzy sets derived from features consisting of normalized distances obtained using the Box approach. The exponential membership function is modified by two structural parameters that are estimated by optimizing the entropy subject to the attainment of membership function to unity. The optimization strategy used is the foraging model of E.coli bacteria. Two window sizes are used: one for ( ) and another for the rest of the numerals. Experimentation is carried out on a limited database of nearly 3500 samples. The overall recognition is found to be 96%.

Color segmentation by fuzzy co-clustering of chrominance color features

Abstract This paper presents a novel color segmentation technique using fuzzy co-clustering approach in which both the objects and the features are assigned membership functions. An objective function which includes a multi-dimensional distance function as the dissimilarity measure and entropy as the regularization term is formulated in the proposed fuzzy co-clustering for images (FCCI) algorithm. The chrominance color cues a ⁎ and b ⁎ of CIELAB color space are used as the feature variables for co-clustering. The experiments are conducted on 100 natural images obtained from the Berkeley segmentation database. It is observed from the experimental results that the proposed FCCI yields well formed, valid and high quality clusters, as verified from Liu’s F -measure and Normalized Probabilistic RAND index. The proposed color segmentation method is also compared with other segmentation methods namely Mean-Shift, NCUT, GMM, FCM and is found to outperform all the methods. The bacterial foraging global optimization algorithm gives image specific values to the parameters involved in the algorithm.

Biometric Authentication Based on Infrared Thermal Hand Vein Patterns

Hand Vein patterns have been adjudged to be one of the safest biometric modalities due to their strong resilience against the impostor attacks. This paper presents a new approach for biometric authentication using infrared thermal hand vein patterns. In contrast to the existing features for hand vein patterns which are based solely on edge detection, we propose Box and branch point based approaches for multiple feature representations. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns and is convolved with Gabor filter. The real part of this convolution is only preserved for further processing. Multiple features are extracted from the real parts of the convolved images using the proposed branch point based feature extraction techniques. The multiple features are then integrated at the decision level. AND and OR fusion rules are employed to combine the decisions taken by the individual matcher. Experiments conducted on a database of 100 users result in a False Acceptance Rate (FAR) of 0.1% for the Genuine Acceptance Rate (GAR) of 99% for decision level fusion.

Choquet fuzzy integral based modeling of nonlinear system

For dealing with the adjacent input fuzzy sets having overlapping information, non-additive fuzzy rules are formulated by defining their consequent as the product of weighted input and a fuzzy measure. With the weighted input, need arises for the corresponding fuzzy measure. This is a new concept that facilitates the evolution of new fuzzy modeling. The fuzzy measures aggregate the information from the weighted inputs using the λ-measure. The output of these rules is in the form of the Choquet fuzzy integral. The underlying non-additive fuzzy model is investigated for identification of non-linear systems. The weighted input which is the additive S-norm of the inputs and their membership functions provides the strength of the rules and fuzzy densities required to compute fuzzy measures subject to q-measure are the unknown functions to be estimated. The use of q-measure is a powerful way of simplifying the computation of @l-measure that takes account of the interaction between the weighted inputs. Two applications; one real life application on signature verification and forgery detection, and another benchmark problem of a chemical plant illustrate the utility of the proposed approach. The results are compared with those existing in the literature.

Blotch Detection in Pigmented Skin Lesions Using Fuzzy Co-clustering and Texture Segmentation

The ‘Fuzzy Co-Clustering Algorithm for Images (FCCI)’ technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. Experiments show the efficacy of the proposed method in extracting malignant blotches from different types of pigmented skin lesion images.

Fusion of Hand Based Biometrics Using Particle Swarm Optimization

Multi-modal biometrics has numerous advantages over uni- modal biometric systems. Decision level fusion is the most popular fusion strategy in multimodal biometric systems. Recent research has shown promising performance of hand based biometrics, i.e. palmprint and hand geometry over other biometric modalities. However, the improvement in performance is constrained by the lack of optimal sensor points and fusion strategy. In this paper, we have implemented a particle swarm based optimization technique for selecting optimal parameters through decision level fusion of two modalities: palmprint and hand geometry. The experimental evaluation on a database of 100 users confirms the utility of the decision level fusion using particle swarm optimization.

Automatic Segmentation and Recognition of Bank Cheque Fields

This paper describes a novel method for automatically segmenting and recognizing the various information fields present on a bank cheque. The uniqueness of our approach lies in the fact that it doesn’t necessitate any prior information and requires minimum human intervention. The extraction of segmented fields is accomplished by means of a connectivity based approach. For the recognition part, we have proposed four innovative features, namely; entropy, energy, aspect ratio and average fuzzy membership values. Though no particular feature is pertinent in itself but a combination of these is used for differentiating between the fields. Finally, a fuzzy neural network is trained to identify the desired fields. The system performance is quite promising on a large dataset of real and synthetic cheque images.