G. Hemantha Kumar

(2D)2LDA: An efficient approach for face recognition

Although 2DLDA algorithm obtains higher recognition accuracy, a vital unresolved problem of 2DLDA is that it needs huge feature matrix for the task of face recognition. To overcome this problem, this paper presents an efficient approach for face image feature extraction, namely, (2D)^2LDA method. Experimental results on ORL and Yale database show that the proposed method obtains good recognition accuracy despite having less number of coefficients.

Designing efficient fusion schemes for multimodal biometric systems using face and palmprint

In this paper, we address the problem of designing efficient fusion schemes of complementary biometric modalities such as face and palmprint, which are effectively coded using Log-Gabor transformations, resulting in high dimensional feature spaces. We propose different fusion schemes at match score level and feature level, which we compare on a database of 250 virtual people built from the face FRGC and the palmprint PolyU databases. Moreover, in order to reduce the complexity of the fusion scheme, we implement a particle swarm optimization (PSO) procedure which allows the number of features (identifying a dominant subspace of the large dimension feature space) to be significantly reduced while keeping the same level of performance. Results in both closed identification and verification rates show a significant improvement of 6% in performance when performing feature fusion in Log-Gabor space over the more common optimized match score level fusion method.

Particle swarm optimization based fusion of near infrared and visible images for improved face verification

This paper presents two novel image fusion schemes for combining visible and near infrared face images (NIR), aiming at improving the verification performance. Sub-band decomposition is first performed on the visible and NIR images separately. In both cases, we further employ particle swarm optimization (PSO) to find an optimal strategy for performing fusion of the visible and NIR sub-band coefficients. In the first scheme, PSO is used to calculate the optimum weights of a weighted linear combination of the coefficients. In the second scheme, PSO is used to select an optimal subset of features from visible and near infrared face images. To evaluate and compare the efficacy of the proposed schemes, we have performed extensive verification experiments on the IRVI database. This database was acquired in our laboratory using a new sensor that is capable of acquiring visible and near infrared face images simultaneously thereby avoiding the need for image calibration. The experiments show the strong superiority of our first scheme compared to NIR and score fusion performance, which already showed a good stability to illumination variations.

Letters: Diagonal Fisher linear discriminant analysis for efficient face recognition

In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods.

Robust Unconstrained Handwritten Digit Recognition using Radon Transform

The performance of a character recognition system depends heavily on what features are being used. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we propose a novel system based on radon transform for handwritten digit recognition. We have used radon function which represents an image as a collection of projections along various directions. The resultant feature vector by applying this method is the input for the classification stage. A nearest neighbor classifier is used for the subsequent recognition purpose. A test performed on the MNIST handwritten numeral database and on Kannada handwritten numerals demonstrate the effectiveness and feasibility of the proposed method

Multilingual OCR system for South Indian scripts and English documents: An approach based on Fourier transform and principal component analysis

Character recognition lies at the core of the discipline of pattern recognition where the aim is to represent a sequence of characters taken from an alphabet [Kasturi, R., Gorman, L.O., Govindaraju, V., 2002. Document image analysis: a primer. Sadhana 27 (Part 1), 3-22]. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we present a multilingual character recognition system for printed South Indian scripts (Kannada, Telugu, Tamil and Malayalam) and English documents. South Indian languages are most popular languages in India and around the world. The proposed multilingual character recognition is based on Fourier transform and principal component analysis (PCA), which are two commonly used techniques of image processing and recognition. PCA and Fourier transforms are classical feature extraction and data representation techniques widely used in the area of pattern recognition and computer vision. Our experimental results show the good performance over the data sets considered.

A novel boundary growing approach for accurate skew estimation of binary document images

Skew angle estimation is an important component of optical character recognition (OCR) systems and document analysis systems (DAS). In this paper, a novel and an efficient method to estimate the skew angle of a scanned document image is proposed. The proposed method has two stages. In first stage, using boundary-growing approach, text lines containing characters of the scanned document image are extracted. From each text line, coordinates of the positions of the characters are obtained. In second stage, the obtained coordinates are fed to linear regression analysis (LRA) for the purpose of computation of skew angle. Several experiments have been conducted on various types of documents such as documents containing different language texts, documents with different fonts and documents with noise to reveal the robustness of the proposed method. A comparative study with the well-known methods is presented to show that the proposed method is superior in terms of accuracy and computational efficiency. fficiency.

A novel technique for estimation of skew in binary text document images based on linear regression analysis

When a document is scanned either mechanically or manually for digitization, it often suffers from some degree of skew or tilt. Skew-angle detection plays an important role in the field of document analysis systems and OCR in achieving the expected accuracy. In this paper, we consider skew estimation of Roman script. The method uses the boundary growing approach to extract the lowermost and uppermost coordinates of pixels of characters of text lines present in the document, which can be subjected to linear regression analysis (LRA) to determine the skew angle of a skewed document. Further, the proposed technique works fine for scaled text binary documents also. The technique works based on the assumption that the space between the text lines is greater than the space between the words and characters. Finally, in order to evaluate the performance of the proposed methodology we compare the experimental results with those of well-known existing methods.

Multisensor biometric evidence fusion of face and palmprint for person authentication using Particle Swarm Optimisation (PSO)

This paper presents a novel biometric sensor fusion technique for face and palmprint images using Particle Swarm Optimisation (PSO). The proposed method can be visualised in the following steps: we first decompose the face and palmprint image obtained from different sensors using wavelet transformation and then, we employ PSO to select most informative wavelet coefficients from face and palmprint to produce a new fused image. We then employed Kernel Direct Discriminant Analysis (KDDA) for feature extraction and the decision about accept/reject is carried out using Nearest Neighbour Classifier (NNC). Extensive experiments carried out on a virtual multimodal biometric database of 250 users indicate the efficacy of the proposed method.

Texture Based Approach for Cloud Classification Using SVM

Cloud analysis provides information which is vital to the detection, understanding and prediction of meteorological trends and environmental changes. Cloud classification is needed for the purpose of achieving an automatic extraction of information on cloud occurrence and cloud types. In this work, we propose a method of cloud classification using textural feature based on Gabor transform and classification is carried out using powerful and elegant classifier known as Support Vector Machines(SVM) using Linear, Polynomial and Gaussian RBF Kernel. Extensive Experiments are carried out on large database of 500 cloud images collected from Kalpana -1 satellite. Experiments have shown the efficacy of the proposed method.