Majid Komeili

Bio: Majid Komeili is an academic researcher from University of Toronto. The author has contributed to research in topics: Feature selection & Feature extraction. The author has an hindex of 10, co-authored 29 publications receiving 367 citations. Previous affiliations of Majid Komeili include Tarbiat Modares University & Carleton University.

Local Feature Selection for Data Classification

Abstract: Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated method for measuring the similarities of a query datum to each of the respective classes is also proposed. The proposed method makes no assumptions about the underlying structure of the samples; hence the method is insensitive to the distribution of the data over the sample space. The method is efficiently formulated as a linear programming optimization problem. Furthermore, we demonstrate the method is robust against the over-fitting problem. Experimental results on eleven synthetic and real-world data sets demonstrate the viability of the formulation and the effectiveness of the proposed algorithm. In addition we show several examples where localized feature selection produces better results than a global feature selection method.

Continuous Authentication Using One-Dimensional Multi-Resolution Local Binary Patterns (1DMRLBP) in ECG Biometrics

Abstract: The objective of a continuous authentication system is to continuously monitor the identity of subjects using biometric systems. In this paper, we proposed a novel feature extraction and a unique continuous authentication strategy and technique. We proposed One-Dimensional Multi-Resolution Local Binary Patterns (1DMRLBP), an online feature extraction for one-dimensional signals. We also proposed a continuous authentication system, which uses sequential sampling and 1DMRLBP feature extraction. This system adaptively updates decision thresholds and sample size during run-time. Unlike most other local binary patterns variants, 1DMRLBP accounts for observations’ temporal changes and has a mechanism to extract one feature vector that represents multiple observations. 1DMRLBP also accounts for quantization error, tolerates noise, and extracts local and global signal morphology. This paper examined electrocardiogram signals. When 1DMRLBP was applied on the University of Toronto database (UofTDB) 1,012 single session subjects database, an equal error rate (EER) of 7.89% was achieved in comparison to 12.30% from a state-of-the-art work. Also, an EER of 10.10% was resulted when 1DMRLBP was applied to UofTDB 82 multiple sessions database. Experiments showed that using 1DMRLBP improved EER by 15% when compared with a biometric system based on raw time-samples. Finally, when 1DMRLBP was implemented with sequential sampling to achieve a continuous authentication system, 0.39% false rejection rate and 1.57% false acceptance rate were achieved.

Liveness Detection and Automatic Template Updating Using Fusion of ECG and Fingerprint

Abstract: Fingerprints have been extensively used for biometric recognition around the world. However, fingerprints are not secrets, and an adversary can synthesis a fake finger to spoof the biometric system. The mainstream of the current fingerprint spoof detection methods are basically binary classifier trained on some real and fake samples. While they perform well on detecting fake samples created by using the same methods used for training, their performance degrades when encountering fake samples created by a novel spoofing method. In this paper, we approach the problem from a different perspective by incorporating electrocardiogram (ECG). Compared with the conventional biometrics, stealing someone’s ECG is far more difficult if not impossible. Considering that ECG is a vital signal and motivated by its inherent liveness, we propose to combine it with a fingerprint liveness detection algorithm. The combination is natural as both ECG and fingerprints can be captured from fingertips. In the proposed framework, the ECG and fingerprint are combined not only for authentication purpose but also for liveness detection. We also examine automatic template updating using ECG and fingerprint. In addition, we propose a stopping criterion that reduces the average waiting time for signal acquisition. We have performed extensive experiments on the LivDet2015 database which is presently the latest available liveness detection database and compare the proposed method with six liveness detection methods as well as 12 participants of LivDet2015 competition. The proposed system has achieved a liveness detection equal error rate (EER) of 4.2% incorporating only 5 s of ECG. By extending the recording time to 30 s, liveness detection EER reduces to 2.6% which is about 4 times better than the best of six comparison methods. This is also about 2 times better than the best results achieved by the participants of the LivDet2015 competition.

Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics

Abstract: Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

A novel hybrid algorithm for binarization of badly illuminated document images

Abstract: In this paper, we present a novel hybrid algorithm for binarization of badly illuminated document images. This algorithm locally enhances the document image and makes the gray levels of text and background pixels separable. Afterward a simple global binarization algorithm binarizes the enhanced image. The enhancement process is a novel method that uses a separate transformation function to map the gray level of each pixel into a new domain. For each pixel, the transformation function is determined using its neighboring pixels gray level. The proposed binarization algorithm is robust for wide variety of degraded document images. Evaluation over a set of degraded document images illustrates the effectiveness of our proposed binarization algorithm.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Human Auditory Evoked Potentials

Abstract: Introduction: Past, Present and Potential Recording Evoked Potentials: Means to an End Frequency-Domain: Music of the Hemispheres Finding Sources: Forwards and Backwards Sounds to Charm the Brain Interpreting the Waveforms: Time and Uncertainty Electrocochleography: From Song to Synapse Auditory Brainstem Responses: Peaks Along the Way Middle Latency Responses - The Brain and the Brawn Auditory Steady State and Following Responses: Dancing to the Rhythms Late Auditory Evoked Potentials: Changing the Things Which Are Endogenous Auditory Evoked Potentials: Attention Must Be Paid Infant Hearing Assessment: Opening Ears Neurotology and Neurology: From Cochlea to Cortex Auditory Neuropathy: When Time is Broke Cochlear Implants: Body Electric Concluding Comments: Beginning to Live

A review of feature selection techniques in structure learning

Abstract: In the last two decades, there has been significant advancement in heuristics for inducing Bayesian belief networks for the purpose of automatic distillation of knowledge from masses of data with target concepts. However, there are various circumstances where we are confronted to fix a set of most influencing variables in modelling of class variable. This arises in provision of confidence measures on set of variables used in the structure learning of data. In this study, we have tweaked empirical as well as theoretical aspects of various feature selection evaluators, their corresponding searching methods under six well known scoring functions in K2 which is a notable structure learning technique in Bayesian belief network. We have come up with some useful findings for overall computationally efficient approach among eleven evaluators. This analysis is useful in inducing better structure from the given dataset in imparting improved performance metric for Bayesian belief network.

Bioelectrical Signal Processing In Cardiac And Neurological Applications

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