# Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification

## TL;DR

This study investigates how unconventional preprocessing techniques, such as color space conversion and image quantization, impact the performance of deep CNNs in face recognition, highlighting the importance of preprocessing choices.

## Contribution

It systematically evaluates the effect of various unconventional preprocessors on CNN face recognition performance, demonstrating their potential to enhance discriminative capability.

## Key findings

- Preprocessing with HE, quantization, rgbGELog, and YCBCR improves CNN face recognition.
- Optimal performance requires combining preprocessing with augmentation and normalization.
- Plane-based quantization increases pixel homogeneity and storage efficiency.

## Abstract

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.

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Source: https://tomesphere.com/paper/1904.00815