This document proposes a bi-modal face recognition framework that uses principal component analysis (PCA) and fisher linear discriminant analysis (FLDA) to build an expression invariant model. It first discusses treating expressions as either noise or extra features. It then outlines using PCA to reduce dimensionality and extract low-dimensional facial features, and FLDA to maximize discrimination between classes. The document provides walkthroughs of how PCA works by identifying patterns in data and reducing dimensions without much information loss. It explains that FLDA finds projections that maximize distance between class means while accounting for within-class scatter. Fusion of modalities is mentioned but not described in detail.