Reducing false positive rate with the help of scene change indicator in deep learning based real-time face recognition systems

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. Researchers continue to work on deep learning-based algorithms to overcome these difficulties. It is essential to develop models that will work with high accuracy and reduce the computational cost, especially in real-time face recognition systems. Deep metric learning algorithms called representative learning are frequently preferred in this field. However, in addition to the extraction of outstanding representative features, the appropriate classification of these feature vectors is also an essential factor affecting the performance. The Scene Change Indicator (SCI) in this study is proposed to reduce or eliminate false recognition rates in sliding windows with a deep metric learning model. This model detects the blocks where the scene does not change and tries to identify the comparison threshold value used in the classifier stage with a new value more precisely. Increasing the sensitivity ratio across the unchanging scene blocks allows for fewer comparisons among the samples in the database. The model proposed in the experimental study reached 99.25% accuracy and 99.28% F-1 score values ​​compared to the original deep metric learning model. Experimental results show that even if there are differences in facial images of the same person in unchanging scenes, misrecognition can be minimized because the sample area being compared is narrowed.