FaceNet
Facial recognition system
From Wikipedia, the free encyclopedia
FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbin, a group of researchers affiliated with Google. The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition.[1] The system uses a deep convolutional neural network to learn a mapping (also called an embedding) from a set of face images to a 128-dimensional Euclidean space, and assesses the similarity between faces based on the square of the Euclidean distance between the images' corresponding normalized vectors in the 128-dimensional Euclidean space. The system uses the triplet loss function as its cost function and introduced a new online triplet mining method. The system achieved an accuracy of 99.63%, which is the highest score to date on the Labeled Faces in the Wild dataset using the unrestricted with labeled outside data protocol.[2]
Structure
Basic structure
The structure of FaceNet is represented schematically in Figure 1.
For training, researchers used input batches of about 1800 images. For each identity represented in the input batches, there were 40 similar images of that identity and several randomly selected images of other identities. These batches were fed to a deep convolutional neural network, which was trained using stochastic gradient descent with standard backpropagation and the Adaptive Gradient Optimizer (AdaGrad) algorithm. The learning rate was initially set at 0.05, which was later lowered while finalizing the model.
Structure of the CNN
The researchers used two types of architectures, which they called NN1 and NN2, and explored their trade-offs. The practical differences between the models lie in the difference of parameters and FLOPS. The details of the NN1 model are presented in the table below.
| Layer | Size-in (rows × cols × #filters) | Size-out (rows × cols × #filters) | Kernel (rows × cols, stride) | Parameters | FLOPS |
|---|---|---|---|---|---|
| conv1 | 220×220×3 | 110×110×64 | 7×7×3, 2 | 9K | 115M |
| pool1 | 110×110×64 | 55×55×64 | 3×3×64, 2 | 0 | — |
| rnorm1 | 55×55×64 | 55×55×64 | 0 | ||
| conv2a | 55×55×64 | 55×55×64 | 1×1×64, 1 | 4K | 13M |
| conv2 | 55×55×64 | 55×55×192 | 3×3×64, 1 | 111K | 335M |
| rnorm2 | 55×55×192 | 55×55×192 | 0 | ||
| pool2 | 55×55×192 | 28×28×192 | 3×3×192, 2 | 0 | |
| conv3a | 28×28×192 | 28×28×192 | 1×1×192, 1 | 37K | 29M |
| conv3 | 28×28×192 | 28×28×384 | 3×3×192, 1 | 664K | 521M |
| pool3 | 28×28×384 | 14×14×384 | 3×3×384, 2 | 0 | |
| conv4a | 14×14×384 | 14×14×384 | 1×1×384, 1 | 148K | 29M |
| conv4 | 14×14×384 | 14×14×256 | 3×3×384, 1 | 885K | 173M |
| conv5a | 14×14×256 | 14×14×256 | 1×1×256, 1 | 66K | 13M |
| conv5 | 14×14×256 | 14×14×256 | 3×3×256, 1 | 590K | 116M |
| conv6a | 14×14×256 | 14×14×256 | 1×1×256, 1 | 66K | 13M |
| conv6 | 14×14×256 | 14×14×256 | 3×3×256, 1 | 590K | 116M |
| pool4 | 14×14×256 | 3×3×256, 2 | 7×7×256 | 0 | |
| concat | 7×7×256 | 7×7×256 | 0 | ||
| fc1 | 7×7×256 | 1×32×128 | maxout p=2 | 103M | 103M |
| fc2 | 1×32×128 | 1×32×128 | maxout p=2 | 34M | 34M |
| fc7128 | 1×32×128 | 1×1×128 | 524K | 0.5M | |
| L2 | 1×1×128 | 1×1×128 | 0 | ||
| Total | 140M | 1.6B |
Triplet loss
A key innovation of the system was the triplet loss function and its associated mining method. This function has since become central in a variety of other one-shot learning problems.
Performance
See also
Further reading
- Rajesh Gopakumar; Karunagar A; Kotegar, M.; Vishal Anand (September 2023). "A Quantitative Study on the FaceNet System": in Proceedings of ICACCP 2023. Singapore: Springer Nature. pp. 211–222. ISBN 9789819942848.
- Ivan William; De Rosal Ignatius Moses Setiadi; Eko Hari Rachmawanto; Heru Agus Santoso; Christy Atika Sari (2019). "Face Recognition using FaceNet (Survey, Performance Test, and Comparison)" in Proceedings of Fourth International Conference on Informatics and Computing. IEEE Xplore. doi:10.1109/ICIC47613.2019.8985786. Retrieved 6 October 2023.
- For a discussion on the vulnerabilities of Facenet-based face recognition algorithms in applications to the Deepfake videos: Pavel Korshunov; Sébastien Marcel (2022). "The Threat of Deepfakes to Computer and Human Visions" in: Handbook of Digital Face Manipulation and Detection From DeepFakes to Morphing Attacks (PDF). Springer. pp. 97–114. ISBN 978-3-030-87664-7. Retrieved 5 October 2023.
- For a discussion on applying FaceNet for verifying faces in Android: Vasco Correia Veloso (January 2022). Hands-On Artificial Intelligence for Android. BPB Publications. ISBN 9789355510242. Amazon