  Deep networks have achieved huge successes in application domains like object
and face recognition. The performance gain is attributed to different facets of
the network architecture such as: depth of the convolutional layers, activation
function, pooling, batch normalization, forward and back propagation and many
more. However, very little emphasis is made on the preprocessors. Therefore, in
this paper, the network's preprocessing module is varied across different
preprocessing approaches while keeping constant other facets of the network
architecture, to investigate the contribution preprocessing makes to the
network. Commonly used preprocessors are the data augmentation and
normalization and are termed conventional preprocessors. Others are termed the
unconventional preprocessors, they are: color space converters; HSV, CIE L*a*b*
and YCBCR, grey-level resolution preprocessors; full-based and plane-based
image quantization, illumination normalization and insensitive feature
preprocessing using: histogram equalization (HE), local contrast normalization
(LN) and complete face structural pattern (CFSP). To achieve fixed network
parameters, CNNs with transfer learning is employed. Knowledge from the
high-level feature vectors of the Inception-V3 network is transferred to
offline preprocessed LFW target data; and features trained using the SoftMax
classifier for face identification. The experiments show that the
discriminative capability of the deep networks can be improved by preprocessing
RGB data with HE, full-based and plane-based quantization, rgbGELog, and YCBCR,
preprocessors before feeding it to CNNs. However, for best performance, the
right setup of preprocessed data with augmentation and/or normalization is
required. The plane-based image quantization is found to increase the
homogeneity of neighborhood pixels and utilizes reduced bit depth for better
storage efficiency.
