In this book the hyperspectral face recognition system is explored in the context of digital signal & image processing techniques. Hyperspectral images contain a wealth of data, but interpreting them requires an understanding of exactly what properties of human face we are trying to measure, and how they relate to the measurements actually made by the hyperspectral sensor. With the availability of hyperspectral face data it is possible to build systems on this. Main focus current research is to use hyperspectral face images in order to recognition the face. Hyperspectral face images with 33 band are used for generation of Vector Quantization based feature vector extraction process. These images are grouped into eleven sub-bands of three images each. Algorithms like Kekre’s Fast Codebook Generation (KFCG) Algorithm and Kekre’s Median Codebook Generation (KMCG) Algorithm are used to generate codebooks for each sub-band and then store into feature vector database. This feature vector set is used for identification of the person. . K-Nearest Neighborhood classifier (K-NN) is used and performance is evaluated, metrics such as EER, SPI, PI are used for benchmarking.