Facial Parts Detection (FPD) approach in conjunction with Vector Quantization (VQ) algorithm are proposed for face recognition. Detecting facial parts, which are nose, both eyes, and mouth, and choosing appropriate dimensions for each part, are done in the preprocessing phase. In the feature extraction phase, four groups for each person, one group for each detected part, are constructed for dimensionality reduction and feature discrimination by considering all parts of all training poses. For further data compression, VQ algorithm is applied to each of the four groups. Finally, Euclidean distance criterion is used to obtain the recognition rates. Four databases, namely, ORL, YALE, FERET, and FEI are used to evaluate the proposed system. Then K-Fold Cross Validation (CV) is used to analyze the results. The proposed system consistently improved the recognition rates as well as the storage requirements. Sample results are given.