Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit
  Precision
William Gazali Jocelyn Michelle Kho Joshua Santoso Williem
In recent years, model quantization for face recognition has gained
prominence. Traditionally, compressing models involved vast datasets like the
5.8 million-image MS1M dataset as well as extensive training times, raising the
question of whether such data enormity is essential. This paper addresses this
by introducing an efficiency-driven approach, fine-tuning the model with just
up to 14,000 images, 440 times smaller than MS1M. We demonstrate that effective
quantization is achievable with a smaller dataset, presenting a new paradigm.
Moreover, we incorporate an evaluation-based metric loss and achieve an
outstanding 96.15% accuracy on the IJB-C dataset, establishing a new
state-of-the-art compressed model training for face recognition. The subsequent
analysis delves into potential applications, emphasizing the transformative
power of this approach. This paper advances model quantization by highlighting
the efficiency and optimal results with small data and training time.