No abstract

In this paper, we present an efficient feature level fusion scheme that we apply on face and palmprint images. The features for each modality are obtained using Log Gabor transform and concatenated to form a fused feature vector. We then use Particle Swarm Optimization (PSO) scheme to reduce the dimension of this vector. Final classification is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on a virtual multimodal biometric database of 250 users built from the face FRGC and the palmprint PolyU databases. We compare the proposed selection method with the well known Adaptive Boosting (AdaBoost) method in terms of both number of features selected and performance. Experimental results in both closed identification and verification rates show that feature fusion improves performance over match score level fusion and also that the proposed method outperforms AdaBoost in terms of reduction of the number of features and facility of implementation.

This paper involves classification of leaves using GLCM (Gray Level Co-occurrence matrix) texture and SVM (Support Vector Machines). GLCM is used for extracting texture feature of leaves. Creating a plant database for quick and efficient classification and recognition is an important step for their conservation. This approach would help to extract useful features of leaf and improve the accuracy of leaf classification. The standard leaf images are subjected to preprocessing. Feature values are extracted from pre-processed image and they are trained and classified. Standard data sets are used for enhancing the properties of the image.

The approaches proposed by eminent researchers have been discussed in the previous sections. Survey on the researches says that, it defines the uniqueness of a given person. And it is possible in the biometric authentication to have collisions between two people who have completely biometric character. Gabor filter can represents the frequency and orientation of similar to those of the human visual System, and they have been found to be particularly appropriate for texture representation and as well as discrimination. There will be a great research on the HOG, it is purely gradient based and captured the object shape information; it can be used to extract the global feature. HOG compute edge gradient of whole image and find orientation of each pixel so it can generate histogram easily. Many of them used the Support vector machine for classification because it avoids the over fitting, and it can built kernel and also it is an approximation to a bound on the test error rate. The theory behind SVM suggests that it should a good idea.

Information fusion is a key step in multimodal biometric systems. Fusion of information can occur at different levels of a recognition system, i.e., at the feature level, matching-score level, or decision level. However, feature level fusion is believed to be more effective owing to the fact that a feature set contains richer information about the input biometric data than the matching score or the output decision of a classifier. The goal of feature fusion for recognition is to combine relevant information from two or more feature vectors into a single one with more discriminative power than any of the input feature vectors. In pattern recognition problems, we are also interested in separating the classes. In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations into the correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pairwise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within the classes. Our proposed method can be used in pattern recognition applications for fusing features extracted from multiple modalities or combining different feature vectors extracted from a single modality. It is noteworthy that DCA is the first technique that considers class structure in feature fusion. Moreover, it has a very low computational complexity and it can be employed in real-time applications. Multiple sets of experiments performed on Palm print and Hand vein datasets, and using different feature extraction techniques.