SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via
  Filter Pruning
Fernando Alonso-Fernandez Kevin Hernandez-Diaz Jose Maria Buades Rubio Josef Bigun
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.