  This paper aims to develop a novel cost-effective framework for face
identification, which progressively maintains a batch of classifiers with the
increasing face images of different individuals. By naturally combining two
recently rising techniques: active learning (AL) and self-paced learning (SPL),
our framework is capable of automatically annotating new instances and
incorporating them into training under weak expert re-certification. We first
initialize the classifier using a few annotated samples for each individual,
and extract image features using the convolutional neural nets. Then, a number
of candidates are selected from the unannotated samples for classifier
updating, in which we apply the current classifiers ranking the samples by the
prediction confidence. In particular, our approach utilizes the high-confidence
and low-confidence samples in the self-paced and the active user-query way,
respectively. The neural nets are later fine-tuned based on the updated
classifiers. Such heuristic implementation is formulated as solving a concise
active SPL optimization problem, which also advances the SPL development by
supplementing a rational dynamic curriculum constraint. The new model finely
accords with the "instructor-student-collaborative" learning mode in human
education. The advantages of this proposed framework are two-folds: i) The
required number of annotated samples is significantly decreased while the
comparable performance is guaranteed. A dramatic reduction of user effort is
also achieved over other state-of-the-art active learning techniques. ii) The
mixture of SPL and AL effectively improves not only the classifier accuracy
compared to existing AL/SPL methods but also the robustness against noisy data.
We evaluate our framework on two challenging datasets, and demonstrate very
promising results. (http://hcp.sysu.edu.cn/projects/aspl/)
