Imran Naseem

• The University of Western Australia

The vulnerability of conventional face recognition systems to face presentation or face spoofing attacks has attracted a great deal of attention from information security, forensic, and biometric communities during the past few years. With the recent advancement and availability of cutting-edge computing technologies, sophisticated and computationa...

Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. Anticancer peptides (ACPs) are the most promising treatment optio...

Face presentation attack detection (PAD) is considered to be an essential and critical step in modern face recognition systems. Face PAD aims at exposing an imposter or an unauthorized person seeking to deceive the authentication system. Presentation attacks are typically made using a fake ID through a digital/printed photograph, video, paper mask,...

Collage, a popular form of visual-content summarization technique is commonly used by internet users and digital artists. Social media usage is a rising trend that significantly affects the increasing demand for collages. The primary source of collage generation is social media, but other sources also generate it. The searching for a required query...

Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. \emph{Anticancer peptides} (ACPs) are the most promising treatment...

The quantum calculus provides an extra degree of freedom to search the local and global minima by inducing a q-parameter. Motivated by this fact, a quantum calculus-based noisy links incremental least mean squares (NL-qILMS) algorithm is proposed. Moreover, for the proposed NL-qILMS, we also devised various time-varying techniques for the selection...

In distributed wireless networks, the adaptation process depends on the information being shared between various nodes. The global minimum, is therefore, likely to be affected when the information shared between the nodes gets corrupted. This could happen due to several reasons namely link failure, noisy environment and erroneous data etc. In this...

A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only...

Diffusion least mean square (LMS) algorithm is a well-known algorithm for distributed estimation where estimation takes place at multiple nodes. However, it inherits slow convergence speed due to its gradient descent-based design. To deal with this challenge, we proposed a modified diffusion LMS with improved convergence performance by employing qu...

The Least Mean Square (LMS) algorithm has a slow convergence rate as it is dependent on the eigenvalue spread of the input correlation matrix. In this research, we solved this problem by introducing a novel adaptive filtering algorithm for complex domain signal processing based on q-derivative. The proposed algorithm is based on Wirtinger calculus...

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law sc...

Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether their proclaimed superiority over conventional algorithms is well-grounded or is a myth as their performance has never been extensively analyzed. In this article, a rigorous analysis of fractional variants of the least me...

Network traffic matrix estimation is an ill-posed linear inverse problem: it requires to estimate the unobservable origin destination traffic flows, X, given the observable link traffic flows, Y, and a binary routing matrix, A, which are such that Y = AX. This is a challenging but vital problem as accurate estimation of OD flows is required for sev...

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab–based me...

Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether the proclaimed superiority over conventional algorithms is well-grounded or is a myth as their performance has never been extensively analyzed. In this article, a rigorous analysis of fractional variants of the least mean...

In this research, a novel adaptive filtering algorithm is proposed for complex domain signal processing. The proposed algorithm is based on Wirtinger calculus and is called as q-Complex Least Mean Square (q-CLMS) algorithm. The proposed algorithm could be considered as an extension of the q-LMS algorithm for the complex domain. Transient and steady...

Network traffic matrix estimation is an ill-posed linear inverse problem: it requires to estimate the unobservable origin destination traffic flows, X, given the observable link traffic flows, Y, and a binary routing matrix, A, which are such that Y=AX. This is a challenging but vital problem as accurate estimation of OD flows is required for sever...

In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed. The proposed method is based on the q-gradient which is also known as Jackson derivative. In contrast to the conventional gradient, which finds the tangent, the q-gradient finds the secant of the function a...

A novel algorithm to extract image features related to human perception of colors is presented in this paper. The proposed profile is based on the generation of a lookup table with thresholds for just-noticeable color difference against all possible colors from RGB color gamut. The profile is further used to reduce the computational cost of image h...

Background Proteins contribute significantly in every task of cellular life. Their functions encompass the building and repairing of tissues in human bodies and other organisms. Hence they are the building blocks of bones, muscles, cartilage, skin, and blood. Similarly, antifreeze proteins are of prime significance for organisms that live in very c...

A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only...

The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in “Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization” [Nonlinear Dyn. 88(2), 839-858 (2017)]. It i...

Herein, we Sadiq, Alishba a new class of stochastic gradient algorithm for Usman, Muhammad identification. The proposed q-least mean fourth (q-LMF) is Khan, Shujaat extension of the least mean fourth (LMF) Naseem, Imran and it is based on the q-calculus which is also known Moinuddin, Muhammad Jackson’s derivative. The Al-Saggaf, Ubaid M. algorithm...

Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation...

In this paper, we propose an efficient design of optimum error nonlinearities (OENL) for adaptive filters which minimizes the steady-state excess mean square error and attains the limit mandated by the Cramer–Rao bound (CRB) of the underlying estimation process. Novelty of the work resides in the fact that the proposed improved optimum error nonlin...

In this work, a new class of stochastic gradient algorithm is developed based on $q$-calculus. Unlike the existing $q$-LMS algorithm, the proposed approach fully utilizes the concept of $q$-calculus by incorporating time-varying $q$ parameter. The proposed enhanced $q$-LMS ($Eq$-LMS) algorithm utilizes a novel, parameterless concept of error-correl...

In distributed networks, the conventional incremental mode of cooperation between the nodes may suffer instability due to two major reasons: (1) large local errors due to accidental problems, and (2) instability due to link failure or noisy link. This causes error propagation through the entire network resulting in divergence. In this research, we...

Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highly nonlinear system and results...

In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent metho...

Channel estimation is an essential part of modern communication systems as it enhances the overall performance of the system. In recent past a variety of adaptive learning methods have been designed to enhance the robustness and convergence speed of the learning process. However, the need for an optimal technique is still there. Herein, for non-Gau...

In this research, we propose a novel fractional gradient descent-based learning algorithm (FGD) for the radial basis function neural networks (RBF-NN). The proposed FGD is the convex combination of the conventional, and the modified Riemann–Liouville derivative-based fractional gradient descent methods. The proposed FGD method is analyzed for an op...

In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences of various AFPs exhibit a high degree of heterogeneity, consequently the prediction of the AFPs is considered to be a challenging task. In this research, we propose to handle this ardu...

The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and analysis in the paper titled: \textquotedblleft \textit{Momentum fractional LMS for power signal pa...

In this paper, we propose a consolidated framework for the automatic selection of the most discriminant subbands for the problem of face recognition. Essentially, the face images are transformed into textures using the linear binary pattern (LBP) approach, these texturized-faces undergo the wavelet packet decomposition resulting in several subband...

The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in ``Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization'' [Nonlinear Dyn. 88(2), 839-858 (2017)]. It...

In this research, we propose a novel algorithm for learning of the recurrent neural networks called as the fractional back-propagation through time (FBPTT). Considering the potential of the fractional calculus, we propose to use the fractional calculus-based gradient descent method to derive the FBPTT algorithm. The proposed FBPTT method is shown t...

In this work, a new class of stochastic gradient algorithm is developed based on $q$-calculus. Unlike the existing $q$-LMS algorithm, the proposed approach fully utilizes the concept of $q$-calculus by incorporating time-varying $q$ parameter. The proposed enhanced $q$-LMS ($Eq$-LMS) algorithm utilizes a novel, parameterless concept of error-correl...

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the prob...

Tracking of a time-varying channel is a challenging task, especially when channel is non-stationary. In this work, we propose a time-varying q-LMS algorithm to efficiently track a random-walk channel. To do so, we first perform tracking analysis of the q-LMS algorithm in a non-stationary environment and then derive the expressions for the transient...

When a signal is attenuated by some interference or additive noise, adaptive filters provide best solution for the recovery of such signals. Based on adaptive filtering several techniques (algorithms) have been developed in the literature. Due to simplicity, stability, low computational cost and better efficiency, Least Mean Square (LMS) algorithm...

In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, the...

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the prob...

In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problems...

In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problems...

In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences of various AFPs exhibit a high degree of heterogeneity, consequently the prediction of the AFPs is considered to be a challenging task. In this research, we propose to handle this ardu...

In this work an optimum error nonlinearity is derived for the channel estimation in the existence of class-A impulsive noise. The main idea of the design is based on minimizing the steady-state error to reach the limit dictated by the Cramer-Rao Lower Bound (CRLB) of the implicit estimation process. By using the proposed method, optimum error nonli...

In this research, the concept of class-specific dictionaries is proposed for iris recognition. Essentially, the query image is represented as a linear combination of training images from each class. The well-conditioned inverse problem is solved using least squares regression and the decision is ruled in favor of the class with the most precise est...

Background The extracellular matrix (ECM) is a dynamic, physiologically active component of all living tissues. It plays a vital role in the functionality of living tissues. The mutation in ECM genes has shown to cause several diseases including cancer. A reliable prediction of the ECM is therefore of prognostic significance. Objective Since the...

In this research an efficient gene selection method called Discriminant Mutual Information (DMI) algorithm is proposed. The DMI algorithm sequentially induces discrimination and relevance to identify the most significant genes for tumor classification. In particular, in the first step the entire gene population is decorrelated by the formation of g...

Inexpensive structured light sensors can capture rich information from indoor scenes, and scene labeling problems provide a compelling opportunity to make use of this information. In this paper we present a novel conditional random field (CRF) model to effectively utilize depth information for semantic labeling of indoor scenes. At the core of the...

In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intra-class and inter-class distributions. Essentially these distributions are used to calculate the class-based entropy for a given subband. This...

This paper presents an audio visual (AV) person identification system using Linear Regression-based Classifier (LRC) for person identification. Class specific models are created by stacking q-dimensional speech and image vectors from the training data. The person identification task is considered a linear regression problem, i.e., a test (speech or...

In this article, we propose a novel method to derive exact closed-form ergodic capacity and outage probability expressions for correlated Rayleigh fading channels with receive diversity. Unlike the existing works, the proposed method employ a simple approach for the capacity and outage analysis for receiver diversity channels operating at different...

This paper proposes an efficient and robust technique for face recognition. The proposed technique includes the Daubechie's wavelet transform D10, Principal Component Analysis (PCA) and Multiscale fusion for face recognition. Features are extracted using the PCA on original and multiscale images. The multiscale fusion is used to combine the results...

In this paper we address the problem of illumination invariant face recognition. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. In the presence of noise, the well-conditioned inverse p...

In this chapter, the authors discuss the problem of face recognition using sparse representation classification (SRC). The SRC classifier has recently emerged as one of the latest paradigm in the context of view-based face recognition. The main aim of the chapter is to provide an insight of the SRC algorithm with thorough discussion of the underlyi...

We address the closed-set problem of speaker identification by presenting a novel sparse representation classification algorithm. We propose to develop an over complete dictionary using the GMM mean super vector kernel for all the training utterances. A given test utterance corresponds to only a small fraction of the whole training database. We the...

In this paper, a novel speaker identification technique using the Dempster-Shafer evidence theory is discussed. The objective is to fuse the complementary information present from different classifiers into a single decision. Here, we use a decreasing function of the distance (of the classifiers) as the belief function. We show that a combined clas...

In this paper we address the problem of robust face recognition by formulating the pattern recognition task as a problem of robust estimation. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace (Barsi and Jacobs, 2003 [1]), we develop a linear model representing a probe image as a linear combin...

In this paper we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class specific galleries. The in...

In this paper we address for the first time, the problem of video-based face recognition in the context of sparse representation classification (SRC). The SRC classification using still face images, has recently emerged as a new paradigm in the research of view-based face recognition. In this research we extend the SRC algorithm for the problem of...

In this paper, physiological biometrics from face are com- bined with behavioral biometrics from speech in video to achieve robust user authentication. The choice of biometrics is motivated by user con- venience and robustness to forgery as it is hard to simultaneously forge these two biometrics. We used the Mel Frequency Cepstral Coefficients for...

In this paper we address for the first time, the problem of user identification using ear biometrics in the context of sparse representation. During the training session the compressed ear images are transformed to vectors to develop a dictionary matrix A [1]. The downsampled probe vector y is used to develop a linear, underdetermined system of equ...

We propose a model based approach for the problem of face localization. Traditionally, images are represented in the RGB color space, which is a 3-dimensional space that includes the illumination factor. However, the human skin color of different ethnic groups has been shown to change because of brightness. We therefore propose to transform the RGB...

Inthispaper, speaker identification using the Dempster-Shafer theory of evidence is discussed. The objective is to use the complementary information present from different clas- sifiers to fuse the classification results into a single deci- sion. Here, we use a decreasing function of the distance (of the classifiers) as our belief function. In the...

We propose in this paper a model based technique for the detection of human faces from rich still color images. Traditionally, color images are represented in the RGB color space. RGB space, however, is not only a 3-dimensional space but also includes brightness or luminance which is not a reliable criterion for skin separation. To avoid the effect...