Effective Face Verification Systems Based on the Histogram of Oriented Gradients and Deep Learning Techniques 

Abstract—In this paper, we proposed a face verification
method. We experiment with a histogram of oriented gradients
description combined with the linear support vector machine
(HOG+SVM) as for the face detection. Subsequently, we applied
a deep learning method called ResNet-50 architecture in face
verification. We evaluate the performance of the face
verification system on three well-known face datasets (BioID,
FERET, and ColorFERET). The experimental results are
divided into two parts; face detection and face verification. First,
the result shows that the HOG+SVM performs very well on the
face detection part and without errors being detected. Second,
The ResNet-50 and FaceNet architectures perform best and
obtain 100% accuracy on the BioID and FERET dataset. They
also, achieved very high accuracy on ColorFERET dataset.