With the continuous progress of human computer interaction, face detection as well as facial expression recognition is gaining the attention of researchers from the fields of security, psychology, image processing, and computer vision. In this area the most challenging thing is to recognize accurate facial expression with minimum time requirement. In this work, our main focus is to minimize the...

Growing interest in Electroencephalogram (EEG) classification brings a need for the development of appropriate analysis and processing techniques. One of the most significant issues associated with EEG analysis is the high contamination of the recorded signals with various artefacts, both from the subject and from equipment interference. This paper discusses the advantages of using temporal Ind...

This paper compares principal component analysis (PCA) and independent component analysis (ICA) in the context of a baseline face recognition system, a comparison motivated by contradictory claims in the literature. This paper shows how the relative performance of PCA and ICA depends on the task statement, the ICA architecture, the ICA algorithm, and (for PCA) the subspace distance metric. It t...

While the FastICA algorithm is a popular procedure for independent component analysis (ICA) and blind source separation, its average convergence behavior has yet to be studied. This paper provides several statistical convergence analyses of the kurtosis-based FastICA algorithm for two-source noiseless mixtures. We derive explicit and approximate expressions for the evolutions of the average val...

In this article, nonstationary mixing and source models are combined for developing new fast accurate algorithms Independent Component or Vector Extraction (ICE/IVE), one of which stands a extension the well-known FastICA. This model allows moving source-of-interest (SOI) whose distribution on short intervals can be (non-)circular (non-)Gaussian. A particular Gaussian assuming tridiagonal covar...

BACKGROUND Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. METHODS A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surfa...

Blind source separation (BSS) is a problem that appears in many research fields. Fast Independent components analysis (FastICA) one of the techniques to solve problem. The researchers have verified effectiveness technique through offline public datasets. development real-time portable system involving such computationally complex requires an efficient hardware implementation FastICA. A Field pr...

We present an application of the fast Independent Component Analysis FastICA method to the COBE-DMR 4yr data. Although the signal-to-noise ratio in the COBE-DMR data is typically ∼ 1, the approach is able to extract the CMB signal with high confidence when working at high galactic latitudes. However, the foreground emission components have too low a S/N ratio to be reconstructed by this method ...

BACKGROUND Genioglossus myoelectric activity is of great significance in evaluating clinical respiratory function. However, there is a tradeoff in genioglossus EMG measurement with respect to accuracy versus convenience. OBJECTIVE This paper presents a way to separate the characteristics of genioglossus myoelectric activity from multi-channel mandible sEMG through independent component analys...

Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of brain damage, for categorizing sleep stages and various central nervous system disorders like seizures and epilepsy. The EEG source signals are mixed however with other signals such as Electrooculog...