MobileFAN: Transferring Deep Hidden Representation for Face Alignment

TL;DR

MobileFAN is a lightweight, efficient face alignment model that uses transfer learning from complex models to improve accuracy while maintaining low computational costs, suitable for real-world applications.

Contribution

The paper introduces MobileFAN, a compact face alignment network leveraging feature distillation from larger models to enhance performance with minimal parameters.

Findings

MobileFAN achieves comparable or better accuracy than state-of-the-art models.

It uses only 8% of the size of traditional models, reducing memory and computational costs.

Extensive experiments validate its effectiveness on multiple benchmarks.

Abstract

Facial landmark detection is a crucial prerequisite for many face analysis applications. Deep learning-based methods currently dominate the approach of addressing the facial landmark detection. However, such works generally introduce a large number of parameters, resulting in high memory cost. In this paper, we aim for a lightweight as well as effective solution to facial landmark detection. To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder. The proposed MobileFAN, with only 8% of the model size and lower computational cost, achieves superior or equivalent performance compared with state-of-the-art models. Moreover, by transferring the geometric structural information of a face graph from a large complex model to our proposed MobileFAN through feature-aligned distillation and feature-similarity distillation, the performance of MobileFAN is further improved in effectiveness and efficiency for face alignment. Extensive experiment results on three challenging facial landmark estimation benchmarks including COFW, 300W and WFLW show the superiority of our proposed MobileFAN against state-of-the-art methods.