The current face recognition (FR) algorithms has achieved a high level of accuracy, making further improvements increasingly challenging. While existing FR algorithms primarily focus on optimizing margins and loss functions, limited attention has been given to exploring the feature representation space. Therefore, this paper endeavors to improve FR performance in the view of feature representation space. Firstly, we consider two FR models that exhibit distinct performance discrepancies, where one model exhibits superior recognition accuracy compared to the other. We implement orthogonal decomposition on the features from the superior model along those from the inferior model and obtain two sub-features. Surprisingly, we find the sub-feature perpendicular to the inferior still possesses a certain level of face distinguishability. We adjust the modulus of the sub-features and recombine them through vector addition. Experiments demonstrate this recombination is likely to contribute to an improved facial feature representation, even better than features from the original superior model. Motivated by this discovery, we further consider how to improve FR accuracy when there is only one FR model available. Inspired by knowledge distillation, we incorporate the intra-class incoherence constraint (IIC) to solve the problem. Experiments on various FR benchmarks show the existing state-of-the-art method with IIC can be further improved, highlighting its potential to further enhance FR performance.