M. Aurangzeb Khan

Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification

In general, shape of an on-line signature is used as a single discriminating feature. Sometimes shape of signature is used alone for verification purposes and sometimes it is used in combination with some other dynamic features such as velocity, pressure and acceleration. The shape of an on-line signature is basically formed due to the wrist and fingers movements where the wrist movement is represented by the horizontal trajectory and the movement of the fingers is represented by vertical trajectory. As the on-line signature is formed due to the combination of two movements that are essentially independent of each other, it will be more effective to use them as two separate discriminating features. Based on this observation, we propose to use these trajectories in isolation by first decomposing the pressure and velocity profiles into two partitions and then extracting the underlying horizontal and vertical trajectories. So the overall process can be thought as the process which exploits the inter-feature dependencies by decomposing signature trajectories depending upon pressure and velocity information and performs verification on each partition separately. As a result, we are able to extract eight discriminating features and among them the most stable discriminating feature is used in verification process. Further Principal Component Analysis (PCA) has been proposed to make the signatures rotation invariant. Experimental results demonstrate superiority of our approach in on-line signature verification in comparison with other techniques.

On-Line Signature Verification by Exploiting Inter-Feature Dependencies

The traditional on-line signature verification process involves use of various dynamic features such as velocity, pressure, acceleration, angles, etc. The idea is to device a composite vector structure combining more than one feature where each feature is treated independently. Our proposed research work is an attempt to exploit the inter-feature dependencies by employing a higher dimensional vector approach. The strategy adopted here is to obtain pressure strokes with respect to various velocity bands. The strokes thus obtained are found to portray a reasonably accurate basis for discriminating genuine vs forgery class. The simulation results validate our assumptions and show improvements in the discriminating index

Signature Verification using Velocity-based Directional Filter Bank

An on-line signature can be characterized with the help of its shape profile and dynamic characteristics. Shape and dynamics are both complementary aspects of a genuine signature. Their relationship should be exploited to develop a highly discriminative feature set for classification purpose. Conventionally these two characteristics are merely concatenated to form feature vector. However, a composite feature vector can be evolved while optimizing discriminative function satisfying shape and dynamics constraints simultaneously. One such simple strategy is suggested in this paper that employ 3D velocity selective directional filter banks. The output of the bank is a vector of directional energy with a given velocity constraint. Such a feature vector has been shown to be effective in developing an on-line signature verification system

On-Line Signature Verification: Directional Analysis of a Signature Using Weighted Relative Angle Partitions for Exploitation of Inter-Feature Dependencies

In this paper, we propose a new directional analysis tool for On-line signatures that decomposes the given input signature into directional bands on the basis of relative angles. Our directional analysis tool takes the independent trajectories (horizontal and vertical) as an input and then decomposes them into directional bands on the basis of relative angles. We have used both user-dependent and user-independent thresholds for selecting an optimal number of partitions for each signer. By decomposing signature trajectories based upon relative angles of an individual’s signature, the resulting process can be thought of as one that exploits inter-feature dependencies . In the verification phase, distances of each partitioned trajectory of a test signature are calculated against a similarly partitioned template trajectory for a known signer. Each partition is then weighted based on its quality and quantity. Experimental results demonstrate the superiority of our approach to On-line signature verification in comparison with other techniques.

Endothelial Cell Image Enhancement using Decimation-Free Directional Filter Banks

The enhancement and binarisation of boundary structure in an endothelial cells image is a vital task for measuring the cell density and degree of hexagonality. Endothelial cells images acquired by a non-contact specular microscope usually suffer from spatially varying brightness, and noisy background. Since the cell image contains boundaries which are predominately linear with various directions. Therefore, the paper proposes directional filter bank to isolate various directional features in the presence of noise. In order to combat non-uniform illumination, the DFB has a pre-processing step of bandpass filters. The combined system has effectively reduced the non-uniform character and noise, while accentuating the linear features. The quality of the results shows promise and proves the effectiveness of our proposed system

On-Line Signature Verification Using 1-D Velocity-Based Directional Analysis

In this paper, we propose a novel approach for identity verification based on the directional analysis of velocity-based partitions of an on-line signature. First, inter-feature dependencies in a signature are exploited by decomposing the shape (horizontal trajectory, vertical trajectory) into two partitions based on the velocity profile of the base-signature for each signer, which offers the flexibility of analyzing both low and high-curvature portions of the trajectory independently. Further, these velocity-based shape partitions are analyzed directionally on the basis of relative angles. Support Vector Machine (SVM) is then used to find the decision boundary between the genuine and forgery class. Experimental results demonstrate the superiority of our approach in on-line signature verification in comparison with other techniques.

Automatic segmentation of pupil using local histogram and standard deviation

This paper presents a novel approach for automatic pupil segmentation. The proposed algorithm uses local histogram and standard deviation based adaptive thresholding method that looks for the region that has the highest probability of having the pupil. We have tested our proposed algorithm on two public databases namely: CASIA v1.0 and MMU v1.0. Experimental results show that the proposed method has satisfying performance and good robustness against the reflection in the pupil.