Information Set based Local Directional Number for Face Recognition

Many algorithms were proposed on face recognition based on the Holistic method, Feature-based method, and also more recently based on local texture patterns. Few local texture patterns utilize the positions of the intensity values like Local Directional Pattern and Local Directional number for obtaining the knowledge (features). This paper proposes the new features based on positions of intensity values and the intensity values in the patch of an image to compute membership function value. The information set concept is used to compute the features that are non-overlapping blocks to restrict the number of features. The proposed method is tested with benchmark databases like ORL and Sheffield and Yale. The classification of the subjects was done with Support Vector Machine (SVM) and K-nearest neighbour Classifier to validate the results. Bio-metric performance curves like Receiver operating Characteristics (ROC) and K-fold validation test is performed. The experimental result shows that the accuracy of recognition has improved over the previously mentioned methods

Conventional Entropy Quantifier and Modified Entropy Quantifiers for Face Recognition

AbstractThis paper presents theoretically simple, yet computationally efficient approach for face recognition. There are many transforms and entropy measures used in face recognition technology. Recognition rate is poor with binary and edge based recognition techniques. We employ the entropy concept to binary and edge images. We use Conventional Entropy Quantifier (CEQ) which counts only the transitions, and Modified Entropy Quantifier (MEQ) which considers the positions with transitions for measuring the entropy. The proposed entropy features possess good texture discriminative property. The experiments are conducted on benchmark databases using SVM and K-NN classifiers. Experimental results show the effectiveness of our system