Despite significant research on lightweight deep neural networks (DNNs) designed for edge devices, the current face detectors do not fully meet the requirements for "intelligent" CMOS image sensors (iCISs) integrated with embedded DNNs. These sensors are essential in various practical applications, such as energy-efficient mobile phones and surveillance systems with always-on capabilities. One noteworthy limitation is the absence of suitable face detectors for the always-on scenario, a crucial aspect of image sensor-level applications. These detectors must operate directly with sensor RAW data before the image signal processor (ISP) takes over. This gap poses a significant challenge in achieving optimal performance in such scenarios. Further research and development are necessary to bridge this gap and fully leverage the potential of iCIS applications. In this study, we aim to bridge the gap by exploring extremely low-bit lightweight face detectors, focusing on the always-on face detection scenario for mobile image sensor applications. To achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs, simulating always-on face detection processed "before" the ISP chain. Our approach employs ternary (-1, 0, 1) weights for potential implementations in image sensors, resulting in a relatively simple network architecture with shallow layers and extremely low-bitwidth. Our method demonstrates reasonable face detection performance and excellent efficiency in simulation studies, offering promising possibilities for practical always-on face detectors in real-world applications.

本研究旨在探索严重低比特的轻量级人脸检测器，重点关注移动图像传感器应用中的始终开启的人脸检测场景，通过利用传感器感知的合成原始输入来模拟ISP链前的始终开启人脸检测。我们的方法采用三值（-1、0、1）权重进行潜在实施，具有相对简单的网络架构、浅层和极低的比特位。我们的方法在模拟研究中展示了合理的人脸检测性能和出色的效率，在实际应用中为实用的始终开启人脸检测器提供了有希望的可能性。

基于移动图像传感器的全量化始终开启人脸检测器