openface/docs/models-and-accuracies.md

Models and Accuracies

This page overviews different OpenFace neural network models and is intended for advanced users.

Model Definitions

The number of parameters are with 128-dimensional embeddings and do not include the batch normalization running means and variances.

Model	Number of Parameters
nn4.small2	3733968
nn4.small1	5579520
nn4	6959088
nn2	7472144

Pre-trained Models

Models can be trained in different ways with different datasets. Pre-trained models are versioned and should be released with a corresponding model definition. Switch between models with caution because the embeddings not compatible with each other.

The current models are trained with a combination of the two largest (of August 2015) publicly-available face recognition datasets based on names: FaceScrub and CASIA-WebFace.

The models can be downloaded from our storage servers:

• nn4.v1
• nn4.v2
• nn4.small1.v1
• nn4.small2.v1

API differences between the models are:

Model	alignment landmarkIndices
nn4.v1	openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
nn4.v2	openface.AlignDlib.OUTER_EYES_AND_NOSE
nn4.small1.v1	openface.AlignDlib.OUTER_EYES_AND_NOSE
nn4.small2.v1	openface.AlignDlib.OUTER_EYES_AND_NOSE

Performance

The performance is measured by averaging 500 forward passes with util/profile-network.lua and the following results use OpenBLAS on an 8 core 3.70 GHz CPU and a Tesla K40 GPU.

Model	Runtime (CPU)	Runtime (GPU)
nn4.v1	75.67 ms ± 19.97 ms	21.96 ms ± 6.71 ms
nn4.v2	82.74 ms ± 19.96 ms	20.82 ms ± 6.03 ms
nn4.small1.v1	69.58 ms ± 16.17 ms	15.90 ms ± 5.18 ms
nn4.small2.v1	58.9 ms ± 15.36 ms	13.72 ms ± 4.64 ms

Accuracy on the LFW Benchmark

Even though the public datasets we trained on have orders of magnitude less data than private industry datasets, the accuracy is remarkably high on the standard LFW benchmark. We had to fallback to using the deep funneled versions for 58 of 13233 images because dlib failed to detect a face or landmarks.

Model	Accuracy	AUC
nn4.small2.v1 (Default)	0.9292 ± 0.0134	0.973
nn4.small1.v1	0.9210 ± 0.0160	0.973
nn4.v2	0.9157 ± 0.0152	0.966
nn4.v1	0.7612 ± 0.0189	0.853
FaceNet Paper (Reference)	0.9963 ± 0.009	not provided

ROC Curves

nn4.small2.v1

nn4.small1.v1

nn4.v2

nn4.v1

Running The LFW Experiment

This can be generated with the following commands from the root openface directory, assuming you have downloaded and placed the raw and deep funneled LFW data from here in ./data/lfw/raw and ./data/lfw/deepfunneled. Also save pairs.txt in ./data/lfw/pairs.txt.

1. Install prerequisites as below.
2. Preprocess the raw lfw images, change 8 to however many separate processes you want to run: for N in {1..8}; do ./util/align-dlib.py data/lfw/raw align outerEyesAndNose data/lfw/dlib-affine-sz:96 --size 96 & done. Fallback to deep funneled versions for images that dlib failed to align: ./util/align-dlib.py data/lfw/raw align outerEyesAndNose data/lfw/dlib-affine-sz:96 --size 96 --fallbackLfw data/lfw/deepfunneled
3. Generate representations with ./batch-represent/main.lua -outDir evaluation/lfw.nn4.small2.v1.reps -model models/openface/nn4.small2.v1.t7 -data data/lfw/dlib-affine-sz:96
4. Generate the ROC curve from the evaluation directory with ./lfw.py nn4.small2.v1 lfw.nn4.small2.v1.reps. This creates roc.pdf in the lfw.nn4.small2.v1.reps directory.

Projects with Higher Accuracy

If you're interested in higher accuracy open source code, see:

Oxford's VGG Face Descriptor

This is licensed for non-commercial research purposes. They've released their softmax network, which obtains .9727 accuracy on the LFW and will release their triplet network (0.9913 accuracy) and data soon (?).

Their softmax model doesn't embed features like FaceNet, which makes tasks like classification and clustering more difficult. Their triplet model hasn't yet been released, but will provide embeddings similar to FaceNet. The triplet model will be supported by OpenFace once it's released.

Deep Face Representation

This uses Caffe and doesn't yet have a license. The accuracy on the LFW is .9777. This model doesn't embed features like FaceNet, which makes tasks like classification and clustering more difficult.