Detection of user-registered dog faces

•We propose a novel framework to adaptively detect user-registered dog faces.•Our detector combines both user-registered samples and off-line trained models.•We design a strategy-selection method to adaptively decide when and how to combine.

Dog face detection is an important object detection task, widely applied in many fields such as auto-focus and image retrieval. In many applications, users only care about specific target species, which are unknown to a detection system until the users register some relevant information like a limited number of target samples. We call this scenario the detection of user-registered dog faces. Due to the great variation between different dog species, no single model can describe all the species well. Meanwhile, it is also impractical to learn individual models for every potential target species that the users may care about, given the large number of dog species. Furthermore, the registered samples are usually too few to train a robust detector directly. In this context, we propose a novel user-registered object detection framework. This framework can generate an adaptive detector, from only a limited number of user-registered target samples and a couple of off-line trained auxiliary models. In addition, we build an annotated dog face dataset, which contains 10,712 images of 32 species. Experimental results on the dataset demonstrate that the proposed framework can achieve superior detection performance to the state-of-the-art approaches.