Face Recognition Using Histogram of Oriented Gradient Features and K Nearest Neighbor Method
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Keywords:
Face Recognition, Histogram of Oriented Gradients, HOG, K-Nearest Neighbor, Machine LearningAbstract
Face recognition system is a technology of identifying people from facial images or video
streams. In general, facial recognition applications are quite beneficial in the fields of security and
identity verification. In this work, facial recognition systems have been implemented via Histogram of
Oriented Gradient features and K-Nearest Neighbor machine learning technique. For this systems, the
facial image dataset, which has four hundred facial images, has been adopted from the well-known data
repository, Kaggle. In preprocessing stage, all the facial images have been applied by Gaussian filter due
to reducing noise levels of the images. In feature extraction stage, The data set has been divided into
training and testing sets via K-Fold-Cross-Validation. For various K values and distance metrics, the
facial recognition systems have been developed with K-Nearest Neighbor using on the training dataset.
For testing of the developed systems, Recall, Precision, Accuracy and F1-Score have been chosen from
the classification metrics. It has been observed that the noise reduction filter improves the success of the
systems.
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