Face recognition has been one of the most important tasks in computer vision due to the wide range of applications in several environments, such as surveillance systems, biometrics, forensics. One of the face recognition tasks, the face verification is responsible for determining whether two facial images belong to the same subject (i.e., genuine matching) or are from different subjects (i.e., an impostor). An example of face verification is the following. Imagine you are at a bank ATM machine, the system will take your picture, extract its features and match them to your biometric information (template) stored in the bank database based on the account number you entered. If there is a positive match, you will have access granted to your account.
Face verification is the focus of our paper entitled Face Verification based on Relational Disparity Features and Partial Least Squares Models that will be presented in the Conference on Graphics, Patterns and Images (SIBGRAPI 2017).
The next figure illustrates how two pairs of images are compared. Once their features are extracted using the VGG-Faces network, they are subtracted element-wise, resulting in the feature vector that will be presented to the classifiers. Such vector is referred to as disparity feature vector.