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# System prepended metadata

title: '(7/19)Computer Vision recent Paper :FaceNet'
tags: [paper]

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# (7/19)Computer Vision recent Paper :FaceNet


###### tags: `paper`
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## Before Meeting
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### Author:
- Florian Schroff
    - ![](https://i.imgur.com/yqAuTRd.png)
    - [refer](https://scholar.google.com/citations?user=eWbZJlMAAAAJ&hl=zh-TW)

- Dmitry Kalenichenko
    - ![](https://i.imgur.com/CT6x3LO.png)
    - [refer]()

- James Philbin
    - ![](https://i.imgur.com/oX7AUH1.png)

    - [refer](https://scholar.google.com/citations?user=80JxPpUAAAAJ&hl=en)






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## Recent Paper

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### FaceNet: A Unified Embedding for Face Recognition and Clustering

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#### Abstracion
- FaceNet
- directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity
-  deep convolutional network
-  only 128-bytes per face.
-  ![](https://i.imgur.com/Yahqmwp.png)

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#### Detail
- Introduction
    - face verification (is this the same person)
    - recognition (who is this person) 
    - clustering (find common people among these faces). 
    - face verification simply involves thresholding the distance between the two embeddings
    - FaceNet directly trains its output to be a compact 128-D embedding using a tripletbased loss function based on LMNN
    -  consist of two matching face thumbnails and a non-matching face thumbnail and the loss aims to separate the positive pair from the negative by a distance margin
    -   online negative exemplar mining strategy

- Method
    -  end-to-end learning of the whole system
    -  triplet loss

- Triplet Loss
    - ![](https://i.imgur.com/kUNQ6HK.png)
    - ![](https://i.imgur.com/AZPebKx.png)
    - ![](https://i.imgur.com/aaUH4CE.png)
- Triplet Selection
- Deep Convolutional Networks
    - Stochastic Gradient Descent (SGD) with standard backprop [8, 11] and AdaGrad
    - parameters
    - FLOPS
    - non-linear activation function
    - The first category: adds 1×1×d convolutional layers
    - The second category we use is based on GoogLeNet style Inception models 
    - ![](https://i.imgur.com/tzZQXR8.png)
- Datasets and Evaluation
    - ![](https://i.imgur.com/WaQLoYJ.png)
    - ![](https://i.imgur.com/NAocbMI.png)
    - ![](https://i.imgur.com/VBq3iaF.png)

- Hold-out Test Set
- Personal Photos
- Academic Datasets
    - Labeled Faces in the Wild (LFW
    - Youtube Faces DB


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#### Conclusion
- Experiments
- Computation Accuracy Trade-off
    - ![](https://i.imgur.com/RgCIO4C.png)
    - Computation Accuracy Trade-off
    - ![](https://i.imgur.com/Y1c1afN.png)
    - ![](https://i.imgur.com/UAc8rly.png)

- Embedding Dimensionality
    - each face is compactly represented by a 128 dimensional byte vector, which is ideal for large scale clustering and recognition
- Amount of Training Data
    - the effect may be even larger on larger models
    - ![](https://i.imgur.com/FvM2Szj.png)
- Effect of CNN Model
    - Overall, in the final performance the top models of both architectures perform comparably. However, some of our Inception based models, such as NN3, still achieve good performance while signif
- Sensitivity to Image Quality
    - ![](https://i.imgur.com/O3MTmvw.png)

- Performance on Youtube Faces DB
    - We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video.This gives us a classification accuracy of 95.12%±0.39.
    - We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video.This gives us a classification accuracy of 95.12%±0.39.
- Performance on LFW
    - We evaluate our model on LFW using the standard protocol for unrestricted, labeled outside data.
    - Our model is evaluated in two modes:
        - Fixed center crop of the LFW provided thumbnail.
        - A proprietary face detector (similar to Picasa) is run on the provided LFW thumbnails. If it fails to align the face (this happens for two images), the LFW alignment is used
- Face Clustering
    - Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity. 
    - ![](https://i.imgur.com/HJr2oJR.png)

- We provide a method to directly learn an embedding into an Euclidean space for face verification
-  only requires minimal alignment (tight crop around the face area)
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