Fast large scale deep face search

Abstract Towards the large scale face search problem, this paper proposes a fast deep face search method which is realized by combination of deep convolution neural network (CNN), semantic hashing, and hash-based similarity search. First of all, to boost the performance in accuracy of face search, the residual network (Resnet) is exploited to construct a deep face feature model and then train it over the cleaned MS-Celeb-1M, which is used to extract real-valued face feature. Next, by imposing PCA and binarization operations, we convert the real-valued feature into compact hash code used for speeding up the face search. Based on the extracted dual features, the face search can be efficiently performed by adopting two-stage matching (i.e., coarse matching and fine matching) strategy. The coarse matching is implemented under the support of efficient hash indexing technique for yielding a small number of candidates while the fine stage is to filter out the unrelated images by cosine distance comparison of real-valued features. It is worth noting that we offer two coarse matching methods, such as GPU-hash and M-index-hash based matching, which are suitable for tens-of million and billion scale scenarios respectively. The experimental results demonstrate that the proposed method is very effective for large scale face search in both aspects of accuracy and real time property.