The widespread use of mobile devices for various digital services has created
a need for reliable and real-time person authentication. In this context,
facial recognition technologies have emerged as a dependable method for
verifying users due to the prevalence of cameras in mobile devices and their
integration into everyday applications. The rapid advancement of deep
Convolutional Neural Networks (CNNs) has led to numerous face verification
architectures. However, these models are often large and impractical for mobile
applications, reaching sizes of hundreds of megabytes with millions of
parameters. We address this issue by developing SqueezerFaceNet, a light face
recognition network which less than 1M parameters. This is achieved by applying
a network pruning method based on Taylor scores, where filters with small
importance scores are removed iteratively. Starting from an already small
network (of 1.24M) based on SqueezeNet, we show that it can be further reduced
(up to 40%) without an appreciable loss in performance. To the best of our
knowledge, we are the first to evaluate network pruning methods for the task of
face recognition.

移动设备的广泛使用为可靠的实时人脸识别验证提供了需求，本研究介绍了一种轻量级的人脸识别网络 SqueezerFaceNet，通过基于 Taylor 分数的网络剪枝方法将网络大小进一步减小，并且在不显著损失性能的情况下证明了其有效性。