Alignface: Enhancing Face Verification Models Through Adaptive Alignment Of Pose, Expression, and Illumination

In the field of face recognition and verification, the practice of face frontalization is conventionally regarded as a standard technique. However, traditional frontalization methods often manipulate original facial images, relying on symmetric cues or data distributions from machine learning model training, which may lead to the distortion of genuine facial features. To tackle these challenges, this paper presents AlignFace, a novel face normalization algorithm specifically designed for preprocessing in the context of face verification. Distinct from existing methods, AlignFace uniquely aligns head pose, expression, and illumination conditions between image pairs. This is achieved by estimating these parameters in one image and reconstructing the other to correspond, all while meticulously preserving each image’s distinct identity features. Such an approach not only ensures a more authentic representation of facial characteristics but also maintains the integrity of real features in one of the images. Our extensive experimental evaluations, conducted on benchmark datasets such as LFW, CFP, AgeDB, and IJB-B, underscore the effectiveness of AlignFace. The comparative analysis with existing methods demonstrates its state-of-the-art performance, highlighting substantial advancements in face verification accuracy. For further research and replication, the code for our method is accessible at: https://github.com/SaharHusseini/ALIGNFACE.