Impact of Facial Expressions on the Accuracy of a CNN Performing Periocular Recognition

The biometric periocular trait refers to the face region in the vicinity of the eye, including the eyelids, eyelashes and eyebrows. The periocular region has emerged as a promising trait for unconstrained biometrics, due to recent advances of convolutional neural networks and the demand for robust face or iris recognition systems. The periocular region can offer global information about the eye shape, and about the texture of the iris, sclera and skin around the eyes. However, periocular biometrics is a relatively new area of research. Thus, it's important to understand the uniqueness and stability of this trait, taking into account the best accuracies obtained by deep learning methods applied on biometric image recognition. In this work, we investigate if changes in the periocular region, caused by facial expressions, affect the recognition accuracy. We apply an existing pretrained CNN architecture, called MobileNet, to the task of periocular recognition. The periocular images used in the experiments were extracted from the Extended Cohn-Kanade expression database. The best results were obtained when the network was tested with similar samples to those contained in the training set. We concluded that the CNN is sensitive to changes in the facial expressions and samples of all expressions are required for training aiming the best accuracy.