A single‐model multi‐task method for face recognition and face attribute recognition in internet of things and visual computing

Abstract

The use of the internet of things (IoT) is steadily increasing in a wide range of applications. Integration of IoT, computer vision, and artificial intelligence can improve people's daily life in various domains such as smart homes, smart cities, and smart industries. There are a large number of face recognition and face attribute recognition scenarios in reality, and the industry commonly decomposes these tasks, with three models responsible for handling face detection, face recognition, and face attribute recognition. The multi‐model approach requires a lot of computational resources for context switching, while training one model with one dataset is not only complex, but also leads to overfitting of the multi‐model approach. The authors propose a single‐model multi‐task approach, which can complete all tasks using only one model, and thus obtains a great improvement in inference speed, especially in scenes with high face density. After an experimental comparison, our approach saves a maximum of 96% of inference time, 49.5% of memory usage, and 59.7% of CPU time, avoids frequent context switching, and simplifies the training steps while improving the generalization performance of the model.

Robust facial landmark detection by probability‐guided hourglass network

“…Facial landmark detection, also known as face alignment, is an essential topic in computer vision and is widely used in many fields, such as face recognition [1], face reconstruction [2], and facial expression recognition [3]. Unlike the traditional features characterized by feature descriptors, facial landmarks include pupils, nose tips, and eye corners, which are visible to the naked eye and have human structural properties.…”