Factorizing and Reconstituting Large-Kernel MBConv for Lightweight Face Recognition

Yaqi Lyu, Jing Jiang, Kun Zhang, Yilun Hua, Miao Cheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

In the past few years, Neural Architecture Search (NAS) has exhibited remarkable advances in terms of neural architecture design, especially on mobile devices. NAS normally use hand-craft MBConv as building block. However, they mainly searched for block-related hyperparameters, and the structure of MBConv itself was largely overlooked. This paper investigates that factorization and reconstitution can promote the efficiency of large-kernel MBConv and thus proposes FR-MBConv (Factorizing and Reconstituting large-kernel MBConv). Compared to large-kernel MBConv with the same receptive field, our FR-MBConv has fewer number of parameters and less computational cost, dramatically increased depth and nonlinearity. In addition, from the perspective of feature generation mechanism, FR-MBConv can be equivalent to more regular convolutions. We combine FR-MBConv with MobileNetV3 to build a lightweight face recognition model. Extensive experiments on face recognition benchmark demonstrate that our lightweight face recognition model outperforms the state-of-the-art lightweight model. Even on large scale face recognition benchmark IJB-B, IJB-C and MegaFace, our lightweight model also achieves comparable performance with large models.