A Novel Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Training Data Reduction in Facial Recognition System

Hassan Opotu Siyaka, Olumide Owolabi, Bisallah I. Hashim

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 a 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. Furthermore, the unavailability of well-annotated data has also affected the success of deep learning facial recognition models. Reducing the training sample sizes of deep learning models is still an open problem. This paper proses in reducing the number of samples required by the convolutional neural network used in training a facial recognition system. A Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA) was developed to select appropriate training samples incrementally based on their facial deviation. The model was tested using an online dataset called Face94 facial image dataset. The results indicated that training a CNN model with at least 3 images per individual produces a 100% accuracy of recognition. This is an indication of the effectiveness of the developed algorithm.


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References


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