Neighborhood Defined Feature Selection Strategy for Improved Face Recognition in Different Sensor Modalitie

A novel feature selection strategy for improved face recognition in images with variations due to illumination conditions, facial expressions, and partial occlusions is presented in this dissertation. A hybrid face recognition system that uses feature maps of phase congruency and modular kernel spaces is developed. Phase congruency provides a measure that is independent of the overall magnitude of a signal, making it invariant to variations in image illumination and contrast. A novel modular kernel spaces approach is developed and implemented on the phase congruency feature maps. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The unique modularization procedure developed in this research takes into consideration that the facial variations in a real world scenario are confined to local regions. The additional pixel dependencies that are considered based on their importance help in providing additional information for classification. This procedure also helps in robust localization of the variations, further improving classification accuracy. The effectiveness of the new feature selection strategy has been demonstrated by employing it in two specific applications via face authentication in low resolution cameras and face recognition using multiple sensors (visible and infrared). The face authentication system uses low quality images captured by a web camera. The optical sensor of the web camera is very sensitive to environmental illumination variations. It is observed that the feature selection policy overcomes the facial and environmental variations. A methodology based on multiple training images and clustering is also incorporated to overcome the additional challenges of computational efficiency and the subject\u27s non involvement. A multi-sensor image fusion based face recognition methodology that uses the proposed feature selection technique is presented in this dissertation. Research studies have indicated that complementary information from different sensors helps in improving the recognition accuracy compared to individual modalities. A decision level fusion methodology is also developed which provides better performance compared to individual as well as data level fusion modalities. The new decision level fusion technique is also robust to registration discrepancies, which is a very important factor in operational scenarios. Research work is progressing to use the new face recognition technique in multi-view images by employing independent systems for separate views and integrating the results with an appropriate voting procedure

Fusing image representations for classification using support vector machines

In order to improve classification accuracy different image representations are usually combined. This can be done by using two different fusing schemes. In feature level fusion schemes, image representations are combined before the classification process. In classifier fusion, the decisions taken separately based on individual representations are fused to make a decision. In this paper the main methods derived for both strategies are evaluated. Our experimental results show that classifier fusion performs better. Specifically Bayes belief integration is the best performing strategy for image classification task.Comment: Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th International Conference, Wellington : Nouvelle-Z\'elande (2009