This document proposes a face recognition system using multimodal and multi-algorithmic feature fusion of hybrid and Kekre wavelet-based feature vectors. The system first pre-processes hyperspectral face images and applies hybrid wavelet transforms and Kekre's wavelet transform to generate feature vectors. These feature vectors are then analyzed using intra-class and inter-class testing to evaluate metrics like true acceptance rate and true rejection rate. The results show that fusing features from multiple algorithms like hybrid wavelet type I, hybrid wavelet type II and Kekre's wavelet transform provides better performance than individual unimodal systems.