  Cross-modal associations between voice and face from a person can be learnt
algorithmically, which can benefit a lot of applications. The problem can be
defined as voice-face matching and retrieval tasks. Much research attention has
been paid on these tasks recently. However, this research is still in the early
stage. Test schemes based on random tuple mining tend to have low test
confidence. Generalization ability of models can not be evaluated by small
scale datasets. Performance metrics on various tasks are scarce. A benchmark
for this problem needs to be established. In this paper, first, a framework
based on comprehensive studies is proposed for voice-face matching and
retrieval. It achieves state-of-the-art performance with various performance
metrics on different tasks and with high test confidence on large scale
datasets, which can be taken as a baseline for the follow-up research. In this
framework, a voice anchored L2-Norm constrained metric space is proposed, and
cross-modal embeddings are learned with CNN-based networks and triplet loss in
the metric space. The embedding learning process can be more effective and
efficient with this strategy. Different network structures of the framework and
the cross language transfer abilities of the model are also analyzed. Second, a
voice-face dataset (with 1.15M face data and 0.29M audio data) from Chinese
speakers is constructed, and a convenient and quality controllable dataset
collection tool is developed. The dataset and source code of the paper will be
published together with this paper.
