Understanding unconventional preprocessors in deep convolutional neural networks for face identification

Olisah, Chollette C.; Smith, Lyndon

Abstract

Deep convolutional neural 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 preprocessor’s module of the network. Therefore, in this paper, the network’s preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the deep 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; grey-level resolution preprocessors; full-based and plane-based image quantization, Gaussian blur, illumination normalization and insensitive feature preprocessors. To achieve fixed network parameters, CNNs with transfer learning is employed. The aim is to transfer knowledge from the high-level feature vectors of the Inception-V3 network to offline preprocessed LFW target data; and features is 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 some of the unconventional preprocessors before feeding it to the CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. Summarily, preprocessing data before it is fed to the deep network is found to increase the homogeneity of neighborhood pixels even at reduced bit depth which serves for better storage efficiency.

Citation

Olisah, C. C., & Smith, L. (2019). Understanding unconventional preprocessors in deep convolutional neural networks for face identification. SN Applied Sciences, 1(11), https://doi.org/10.1007/s42452-019-1538-5

• Journal Article Type: Article
• Acceptance Date: Oct 3, 2019
• Online Publication Date: Oct 30, 2019
• Publication Date: 2019-11
• Deposit Date: Jun 29, 2021
• Journal: SN Applied Sciences
• Electronic ISSN: 2523-3971
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Issue: 11
• Article Number: 1511
• DOI: https://doi.org/10.1007/s42452-019-1538-5
• Public URL: https://uwe-repository.worktribe.com/output/7499108
• Additional Information: Received: 6 June 2019; Accepted: 3 October 2019; First Online: 30 October 2019; : ; : The authors declare that they have no conflict of interest.