The standard multi-scale, multi-orientation Gabor filter ensemble (SGFE) in the face recognition task reposits 40 filters localized in 8 orientations and 5 scales, with a real and an imaginary constituent. This paper devises a simple means of filter diversification, dubbed as multi-fold Gabor filter convolution (-FGFC), where a set of pre-selected filters, e.g., single-scale Gabor filters across varying orientations, are self-cross convolved by folds to instantiate the offspring filters. To facilitate filter selection for-FGFC, this paper summarizes SGFE into the condensed Gabor filter ensemble (CGFE) of only 8 filters. In addition, an average histogram pooling operator is proposed to downsample and regulate the demodulated Gabor phase features prior to the final compression stage. The performance of a specific M-FGFC instance, i.e., the 2-FGFC descriptor, is investigated on FERET I (frontal), FERET II (nonfrontal) and AR datasets. The experimental results on FERET I substantiates that the 2-FGFC descriptor outperforms the leading state of the art face descriptors.