Augmenting the Training Database With the Method of Gradual Similarity Ratios in the Face Recognition Systems

Abstract

In face recognition systems, light direction, reflection, and emotional and physical changes on the face are some of the main factors that make recognition difficult. Deep metric learning algorithms called representative learning are frequently preferred in this field. However, in addition to the model's success in feature extraction, factors such as the distribution of samples in this database and appropriate classifier preferences also affect the overall performance of the face recognition system. This study it is aimed to create integrity in the database of a pre-trained deep neural network model by obtaining augmented data for classes with a limited number of samples. Thanks to this method called Graded Similarity Rates (GSR), augmented data that could disrupt class integrity has been removed from the database. This way, classes with limited examples are kept integrity, and classifier behavior is used more effectively. The model proposed in the experimental study reached 99.38% accuracy values compared to traditional data augmentation models. Experimental results have shown that the database has an acceptable level of success even at smaller vector sizes and is more organized.