This paper presents a novel image denoising technique by using Principal Component Analysis (PCA) and Wavelet transform. The noisy image can be decomposed by the PCA into different blocks. Eigen values for each block is calculated and the common vector from each block is eliminated. The noise under consideration is AWGN and is treated as a Gaussian random variable. The denoised image obtained b...

In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as there are several kernel functions which can be used within PCA-based algorithms. In this paper, however, another side of PCA-based algorithms -par...

Two dimensional principal component analysis (2DPCA) has been proposed for face recognition as an alternative to traditional PCA transform [1]. In this paper, we extend this approach to the visual feature extraction for audio-visual speech recognition (AVSR). First, a two-stage 2DPCA transform is conducted to extract the visual features. Then, the visemic linear discriminant analysis (LDA) is a...

This paper presents powerful techniques for the development of a new monitoring method based on multi-scale entropy (MSE) in order to characterize the behaviour of the concentrations of different gases present in the synthesis of Ammonia and soft-sensor based on Principal Component Analysis (PCA). Keywords—Ammonia synthesis, concentrations of different gases, soft sensor, multi-scale entropy, m...

Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we propose performance analysis of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) for face recognition. This analysis was carried out on various current PCA, LDA and LPP based...

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA and LDA some weaknesses. In this paper, we propose a new Line-based methodes called Line-based PCA and Line-based LDA that ...

Human gait recognition is a developing biometric engineering now a days. It perceives the individual from its walk and above all from a distance without subject’s cooperation. As human gait recognition system is influenced by diverse view variations effects. So, in this paper we have proposed a human gait recognition strategy for the images caught from distinctive viewing edges (0, 45, 90 degre...

We use principal component analysis (PCA) to identify exons of a gene and further analyze their internal structures. The PCA is conducted on the short-time Fourier transform (STFT) based on the 64 codon sequences and the 4 nucleotide sequences. By comparing to independent component analysis (ICA), we can differentiate between the exon and intron regions, and how they are correlated in terms of ...

The features of human lip motion from video clips are extracted by three unsupervised learning algorithms, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). Since the human perception of facial motion goes through two different pathways, i.e., the lateral fusifom gyrus for the invariant aspects and the superior temporal ...