Multiple Face Recognition Using Local Features and Swarm Intelligence

Face Recognition Using Local and Global Features

The combining classifier approach has proved to be a proper way for improving recognition performance in the last two decades. This paper proposes to combine local and global facial features for face recognition. In particular, this paper addresses three issues in combining classifiers, namely, the normalization of the classifier output, selection of classifier(s) for recognition, and the weigh...

Face Detection using Swarm Intelligence

Face Recognition Using Boosted Local Features

This paper presents a new method for face recognition which learns a face similarity measure from example image pairs. A set of computationally efficient “rectangle” features are described which act on pairs of input images. The features compare regions within the input images at different locations, scales, and orientations. The AdaBoost algorithm is used to train the face similarity function ...

Face recognition using multiple facial features

We propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensionality reduction and construction of individual feature spaces. Recognition is done by probabilistically fusing the confidence weights derived from each feature space. The performa...

Face Recognition Using Particle Swarm Optimization-Based Selected Features

Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PS...