Training triplet networks with large-scale data is challenging in face recognition. Due to the number of possible triplets explodes with the number of samples, previous studies adopt the online hard negative mining(OHNM) to handle it. However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found. To solve the problem, in this paper, we propose training triplet networks with subspace learning, which splits the space of all identities into subspaces consisting of only similar identities. Combined with the batch OHNM, hard triplets can be found much easier. Experiments on the large-scale MS-Celeb-1M challenge with 100K identities demonstrate that the proposed method can largely improve the performance. In addition, to deal with heavy noise and large-scale retrieval, we also make some efforts on robust noise removing and efficient image retrieval, which are used jointly with the subspace learning to obtain the state-of-the-art performance on the MS-Celeb-1M competition (without external data in Challenge1).
Inspired by the efficiency of classification, some studies @cite_10 @cite_2 @cite_13 @cite_4 combine the advantages of classification and hard triplets. Wang al. @cite_10 use a pretrained classification model to select possible hard triplets offline, but the offline selection is fixed as the classification model will not be updated. To achieve faster training and handle variant triplets, Parkhi al. @cite_2 train a classification network that is further fine-tuned with triplet loss. They use the Online Hard Negative Mining(OHNM), wherein only the triplets violating the margin constraint are considered as the hard ones for learning. Instead of fine-tuning with only triplet loss, Chen al. @cite_13 propose to train networks jointly with softmax and triplet loss to preserve both inter-class and intra-class information, and they also adopt OHNM in training. To apply OHNM in large-scale data, Schroff al. propose the FaceNet @cite_4 that trains triplet networks with @math identities, and it takes a few months to finish with a large batchsize of @math . One limitation of OHNM is that triplets are predefined in the batch, and this will miss possible hard negative samples that contained in the batch.
@cite_13 @cite_10 @cite_4 @cite_2
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Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is also proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models. Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors. The goal of this paper is face recognition – from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks.