Enhancement Techniques for Improving Facial Recognition Performance in Convolutional Neural Networks
Sharif, S., Olusegun, M., Zorto, A. and Elmedany, W. 2022. Enhancement Techniques for Improving Facial Recognition Performance in Convolutional Neural Networks. 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT 2022). Bahrain, University of Bahrain 20 - 21 Nov 2022 IEEE.
|Authors||Sharif, S., Olusegun, M., Zorto, A. and Elmedany, W.|
The advent of convolutional neural networks (CNNs) to the development of face recognition system has been a game changer in the field of computer vision and pattern recognition. This research work uses a pre trained MobileNet-V1 model to develop an effective CNN model capable of high performance. We also tackle several common facial recognition challenges which include occlusions, illumination variations, make-ups, pose variation and ageing through the use of several improvement techniques. The techniques include adopting a less computationally costly approach, transfer learning and hyper-parameter finetuning. The Top-1 accuracy 70.6% and Top-5 accuracy 89.5% of the base MobileNet-V1 model has been improved using these techniques to achieve training accuracy of 95% and accuracies of 96.4%, 98.0% and 99.1% on the Pins face recognition dataset, FaceScrub data-set and LFW data-set, respectively. The work done so far illustrates the need for further research into improvement techniques for convolutional neural networks.
|Conference||2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT 2022)|
|Accepted author manuscript|
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|Publication process dates|
|Deposited||12 Sep 2022|
|Copyright holder||© 2022 IEEE|
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