Muhammad Talal Ibrahim

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.

Multimodal information fusion for selected multimedia applications

The effective interpretation and integration of multiple information content are important for the efficacious utilisation of multimedia in a wide variety of application context. The major challenge in multimodal information fusion lies in the difficulty of identifying the complementary and discriminatory representations from individual channels, and the efficient fusion of the resulting information for the targeted application problem. This paper outlines several multimedia systems that utilise a multimodal approach, and provides a comprehensive review of the state-of-the-art in related areas, including emotion recognition, image annotation and retrieval, and biometrics. Data collected from diverse sources or sensors are employed to improve the recognition or classification accuracy. It is shown that the combination of multimodality information is capable of providing a more complete and effective description of the intrinsic characteristics of the specific pattern, and producing improved system performance than single modality only. In addition, we present a facial fiducial point detection and a gesture recognition system, which can be incorporated into a multimodal framework. The issues and challenges in the research and development of multimodal systems are discussed, and a cutting-edge application of multimodal information fusion for intelligent robotic system is presented.

On-line signature verification using global features

On-line signature verification based on global features in an integration with Fisher Linear Discriminant Analysis (FLD) have been proposed in this paper. In the verification phase, distances of features of test signature are calculated against their corresponding template. Finally, these distances become inputs to FLD. User-dependent threshold has been used to evaluate the performance of our proposed method in comparison to other existing methods. We have used single-session and mix-session protocols for the evaluation of our proposed method using SUSIG database. Experimental results demonstrate the superiority of our approach in On-line signature verification in comparison with other techniques.

On-line Signature Verification Using Most Discriminating Features and Fisher Linear Discriminant Analysis (FLD)

In this work, we employ a combination of strategies for partitioning and detecting abnormal fluctuations in the horizontal and vertical trajectories of an on-line generated signature profile. Alternative partitions of these spatial trajectories are generated by splitting each of the related angle, velocity and pressure profiles into two regions representing both high and low activity. The overall process can be thought of as one that exploits inter-feature dependencies by decomposing signature trajectories based upon angle, velocity and pressure - information quite characteristic to an individualpsilas signature. 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. Finally, these distances become inputs to Fisherpsilas Linear Discriminant Analysis (FLD). Experimental results demonstrate the superiority of our approach in On-line signature verification in comparison with other techniques.

Creation and selection of most stable discriminating features for on-line signature verification

In this paper, we propose a method for exploiting the dependence of four dynamic features (horizontal trajectory, vertical trajectory, pressure and velocity) upon angles of each sample point from the mean of the signature. We have achieved this purpose by decomposing each of these four dynamic features into two partitions. As a result we obtain eight discriminating features, out of which we have selected two most stable features on the basis of training signatures scatter on two dimensional dissimilarity space. These two most stable features are then employed for verification purpose. Result shows the promise of our proposed technique.

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.

A Filter Bank Based Approach for Rotation Invariant Fingerprint Recognition

This paper presents a systematic approach for image-based fingerprint recognition. The proposed method first enhances an input fingerprint image using a contextual filtering based method in the frequency domain. Complex filters are used for the detection of the core point, and a region of interest (ROI) of a predefined size centered at the detected core point is extracted. The resulting ROI is rotated based on the angle of the detected core point to ensure rotation invariance. Subsequently, the proposed system extracts the average absolute deviation (AAD) from the outputs of a Gabor filter bank. To reduce the dimensionality of the extracted features whilst generating more discriminatory representation, this paper compares the unsupervised Principal Component Analysis (PCA) and the supervised Linear Discriminant Analysis (LDA) methods for dimensionality reduction. User-specific thresholding schemes are investigated to improve the verification performance. The effectiveness of the proposed method is demonstrated through extensive experimentation on the FVC2002 set_a public database, in both identification and verification scenarios.

Robust Fingerprint Image Enhancement: An Improvement to Directional Analysis of Fingerprint Image Using Directional Gaussian Filtering and Non-subsampled Contourlet Transform

In this paper, a new fingerprint image enhancement method based on the integration of directional Gaussian filtering and non-subsampled contourlet transform (NSCT) has been proposed. As the fingerprint images suffers from non-uniform illumination, so there is a need to completely remove or reduce the non-uniform illumination present in the fingerprint image before applying any enhancement technique. In this paper, we have used bandpass filter for the elimination of non-uniform illumination and for the creation of frequency ridge image. Further the directional noise present in the fingerprint image have been removed by using the directional Gaussian filter and the noise-free image was decomposed into non-subsampled directional images by using NSCT. Lately, we construct an enhanced image through a block-by-block process which compares the energy of all the directional images and picks the one that provides maximum energy. Further, binarization and thinning are also applied on the enhanced image.

Hybrid Vector Filters based on Marginal Ordering for Impulsive Noise Suppression in Color Images

To attenuate impulsive noise in color images, a hybrid basic vector filter and its switching extensions are introduced in this paper. By utilizing reliable components provided by the marginal filter and retaining the inherent correlation between multi-channels, the new method selects the vector, which has minimal distance to the output of the marginal median filter. Based on this scheme, some well-known switching filters are easily modified to improve their noise suppression capability. The experiments demonstrate that the proposed filtering approach is more effective to suppress multichannel impulsive noise in color images, and its computation is more efficient than state-of-the-art basic vector filters. Moreover, extended experiments indicate that the noise suppression capability of several well-known switching vector filters can also be improved by adopting this basic scheme as an alternative approach at the replacement stage.

Robust signal generation and analysis of rat embryonic heart rate in vitro using laplacian eigenmaps and empirical mode decomposition

To develop an accurate and suitable method for measuring the embryonic heart rate in vitro, a system combining Laplacian eigenmaps and empirical mode decomposition has been proposed. The proposed method assess the heart activity in two steps; signal generation and heart signal analysis. Signal generation is achieved by Laplacian eigenmaps (LEM) in conjunction with correlation co-efficient, while the signal analysis of the heart motion has been performed by the modified empirical mode decomposition (EMD). LEM helps to find the template for the atrium and the ventricle respectively, whereas EMD helps to find the non-linear trend term without defining any regression model. The proposed method also removes the motion artifacts produced due to the the non-rigid deformation in the shape of the embryo, the noise induced during the data acquisition, and the higher order harmonics. To check the authenticity of the proposed method, 151 videos have been investigated. Experimental results demonstrate the superiority of the proposed method in comparison to three recent methods.