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The widespread use of mobile devices for various digital services has createda need for reliable and real-time person authentication. In this context,facial recognition technologies have emerged as a dependable method forverifying users due to the prevalence of cameras in mobile devices and theirintegration into everyday applications. The rapid advancement of deepConvolutional Neural Networks (CNNs) has led to numerous face verificationarchitectures. However, these models are often large and impractical for mobileapplications, reaching sizes of hundreds of megabytes with millions ofparameters. We address this issue by developing SqueezerFaceNet, a light facerecognition network which less than 1M parameters. This is achieved by applyinga network pruning method based on Taylor scores, where filters with smallimportance scores are removed iteratively. Starting from an already smallnetwork (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 ourknowledge, we are the first to evaluate network pruning methods for the task offace recognition.

SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning