  The local descriptors have been the backbone of most of the computer vision
problems. Most of the existing local descriptors are generated over the raw
input images. In order to increase the discriminative power of the local
descriptors, some researchers converted the raw image into multiple images with
the help of some high and low pass frequency filters, then the local
descriptors are computed over each filtered image and finally concatenated into
a single descriptor. By doing so, these approaches do not utilize the inter
frequency relationship which causes the less improvement in the discriminative
power of the descriptor that could be achieved. In this paper, this problem is
solved by utilizing the decoder concept of multi-channel decoded local binary
pattern over the multi-frequency patterns. A frequency decoded local binary
pattern (FDLBP) is proposed with two decoders. Each decoder works with one low
frequency pattern and two high frequency patterns. Finally, the descriptors
from both decoders are concatenated to form the single descriptor. The face
retrieval experiments are conducted over four benchmarks and challenging
databases such as PaSC, LFW, PubFig, and ESSEX. The experimental results
confirm the superiority of the FDLBP descriptor as compared to the
state-of-the-art descriptors such as LBP, SOBEL_LBP, BoF_LBP, SVD_S_LBP, mdLBP,
etc.
