Catch them Unattentive: An Orientation Aware Face Recognition Model

Identifying people’s identity from a group photo through face recognition models has applications in various fields. There are two major challenges, first due to the presence of several faces with various degrees of clarity and scale, and second due to angular orientation of faces in usual group photos. Detecting and cropping the faces have been reasonably solved using various segmentation-like models. Recognizing identity after cropping a frontal face has also been successful to some extent. However, the presence of orientation, often manifested by yaw and pitch reduces identifying features from faces. In such cases, models often make mistakes if they are forced to match a face with some identity. Our objective is to modulate the loss based on the orientation angle and consider the fact into the model that it is harder to detect if the angle of orientation is more. This approach leads to better accuracy and is also more intuitive. None of the existing methods in the literature address this problem, and when we compare with them we found the proposed model to outperform them on several datasets.