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

Face Detection Technique Based on Rotation Invariant Wavelet Features

A rotation and scale invariant face detection algorithm based on the texture of a human face is proposed in the paper. Wavelet packet analysis is performed on the test image to get the coefficients. It is observed that wavelet packet decomposition until third level is sufficient enough to get the necessary frequencial components essential for classifying faces and non-faces. Rotation invariant textural features are extracted from the wavelet coefficients. A scale invariant distance measure between the feature vectors of each of the candidate faces and the prototype face image is proposed to classify the candidate faces into faces and non-faces. The detection process implements a non-linear luminance based lighting compensation method, which is very efficient in enhancing and restoring the natural colors into the images taken in darker and varying lighting environments. The novel detection process proposed is highly efficient in terms of speed and accuracy in detecting frontal view faces in a complex background. The face detection performance of the proposed system is comparable to other reported leading system