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.

/pdf/facenet-a-unified-embedding-for-face-recognition-and-135p2gog88.pdf

FaceNet: A unified embedding for face recognition and clustering

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

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DeepFace: Closing the Gap to Human-Level Performance in Face Verification

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.

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Deep face recognition

Despite significant recent advances in the field of face recognition, 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. 
Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-art face recognition performance using only 128-bytes per face. 
On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves 95.12%. Our system cuts the error rate in comparison to the best published result by 30% on both datasets. 
We also introduce the concept of harmonic embeddings, and a harmonic triplet loss, which describe different versions of face embeddings (produced by different networks) that are compatible to each other and allow for direct comparison between each other.

FaceNet: A Unified Embedding for Face Recognition and Clustering

One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that can enhance the discriminative power. Centre loss penalises the distance between deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in the angular space and therefore penalises the angles between deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximise face class separability. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to its exact correspondence to geodesic distance on a hypersphere. We present arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks which includes a new large-scale image database with trillions of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead. To facilitate future research, the code has been made available.

/pdf/arcface-additive-angular-margin-loss-for-deep-face-7vvlem659h.pdf

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., ‘in the wild’), the ‘YouTube Faces’ database, along with benchmark, pair-matching tests1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.

/pdf/face-recognition-in-unconstrained-videos-with-matched-3tbbtju4in.pdf

Face recognition in unconstrained videos with matched background similarity

For efficient high availability for a multi-node cluster using a processor device in a computing environment, using duplicate, standby host-bus adaptors (HBAs) for alternate nodes with respect to a node with the duplicate, standby HBAs using duplicate credentials of active HBAs of the node for shutting down the node, taking an active HBA of the node offline, and/or activating one of the alternate nodes.

Efficient high availability for a scsi target over a fibre channel

Effects of gibberellin and cytokinin on phenolic and volatile composition of Sangiovese grapes

For coordinated disaster recovery, a reconciliation process is performed for resolving intersecting and non-intersecting data amongst disaster recovery systems for takeover operations. An ownership synchronization process is coordinated for replica cartridges via the reconciliation process at the disaster recovery systems. The disaster recovery systems continue as a replication target for source systems and as a backup target for local backup applications.

Coordinated disaster recovery production takeover operations

Abstract Camptothecin is a chemotherapeutic drug widely used to treat various cancers. Ophiorrhiza pumila is an ideal plant model for the study of camptothecin production, with various advantages for studying camptothecin biosynthesis and regulation. The DNA-binding WRKY transcription factors have a key regulatory role in secondary metabolite biosynthesis in plants. However, little is currently known about their involvement in camptothecin biosynthesis in O. pumila. We identified 46 OpWRKY genes unevenly distributed on the 11 chromosomes of O. pumila. Phylogenetic and multiple sequence alignment analyses divided the OpWRKY proteins into three subfamilies. Based on spatial expression and co-expression, we targeted the candidate gene OpWRKY6. Overexpression of OpWRKY6 significantly reduced the accumulation of camptothecin compared with the control. Conversely, camptothecin accumulation increased in OpWRKY6 knockout lines. Further biochemical assays showed that OpWRKY6 negatively regulates camptothecin biosynthesis from both the iridoid and shikimate pathways by directly downregulating the gene expression of OpGES, Op10HGO, Op7DLH, and OpTDC. Our data provide direct evidence for the involvement of WRKYs in the regulation of camptothecin biosynthesis and offer valuable information for enriching the production of camptothecin in plant systems.

/pdf/identification-of-wrky-transcription-factors-involved-in-3qw2h9sg.pdf

Itay Maoz

Papers

Face recognition in unconstrained videos with matched background similarity

Efficient high availability for a scsi target over a fibre channel

Effects of gibberellin and cytokinin on phenolic and volatile composition of Sangiovese grapes

Coordinated disaster recovery production takeover operations

Identification of WRKY transcription factors involved in regulating the biosynthesis of the anti-cancer drug camptothecin in Ophiorrhiza pumila