| Title | A New Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Facial Image Recognition Systems: The Mathematical Models |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Siyaka, Hassan Opotu, Owolabi, Olumide, Bisallah, I. Hashim |
| Conference Name | 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS) |
| Keywords | convolutional neural network, convolutional neural networks, Deep Learning, Estimation, face recognition, Facial Image Deviation Estimation, facial recognition, Human Behavior, image recognition, mathematical models, Metrics, pubcrawl, resilience, Resiliency, Training |
| Abstract | Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above. |
| DOI | 10.1109/ICMEAS52683.2021.9739810 |
| Citation Key | siyaka_new_2021 |
A New Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Facial Image Recognition Systems: The Mathematical Models