This paper explores the extraction of features from color imagery for recognition tasks, especially face recognition. The well-known Gabor filter, which is typically defined as a complex function, has been extended to the hypercomplex (quaternion) domain. Several proposed modes of this extension are discussed, and a preferred formulation is selected. To quantify the effectiveness of this novel filter for colorbased feature extraction, an elastic graph implementation for human face recognition has been extended to color images, and performance of the corresponding monochromatic and color recognition systems have been compared. Our experiments have shown an improvement of 3% to 17% in recognition accuracy over the analysis of monochromatic images using complex Gabor filters.