Face Recognition

Qualeams

About this app

Face Recognition can be used as a test framework for several face recognition methods including the Neural Networks with TensorFlow and Caffe.

It includes following preprocessing algorithms:
- Grayscale
- Crop
- Eye Alignment
- Gamma Correction
- Difference of Gaussians
- Canny-Filter
- Local Binary Pattern
- Histogramm Equalization (can only be used if grayscale is used too)
- Resize

You can choose from the following feature extraction and classification methods:
- Eigenfaces with Nearest Neighbour
- Image Reshaping with Support Vector Machine
- TensorFlow with SVM or KNN
- Caffe with SVM or KNN

The manual can be found here https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning/blob/master/USER%20MANUAL.md

At the moment only armeabi-v7a devices and upwards are supported.

For best experience in recognition mode rotate the device to left.
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TensorFlow:

If you want to use the Tensorflow Inception5h model, download it from here:
https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip

Then copy the file "tensorflow_inception_graph.pb" to "/sdcard/Pictures/facerecognition/data/TensorFlow"

Use these default settings for a start:
Number of classes: 1001 (not relevant as we don't use the last layer)
Input Size: 224
Image mean: 128
Output size: 1024
Input layer: input
Output layer: avgpool0
Model file: tensorflow_inception_graph.pb
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If you want to use the VGG Face Descriptor model, download it from here:
https://www.dropbox.com/s/51wi2la5e034wfv/vgg_faces.pb?dl=0

Caution: This model runs only on devices with at least 3 GB or RAM.

Then copy the file "vgg_faces.pb" to "/sdcard/Pictures/facerecognition/data/TensorFlow"

Use these default settings for a start:
Number of classes: 1000 (not relevant as we don't use the last layer)
Input Size: 224
Image mean: 128
Output size: 4096
Input layer: Placeholder
Output layer: fc7/fc7
Model file: vgg_faces.pb
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Caffe:

If you want to use the VGG Face Descriptor model, download it from here:
http://www.robots.ox.ac.uk/~vgg/software/vgg_face/src/vgg_face_caffe.tar.gz

Caution: This model runs only on devices with at least 3 GB or RAM.

Then copy the files "VGG_FACE_deploy.prototxt" and "VGG_FACE.caffemodel" to "/sdcard/Pictures/facerecognition/data/caffe"

Use these default settings for a start:
Mean values: 104, 117, 123
Output layer: fc7
Model file: VGG_FACE_deploy.prototxt
Weights file: VGG_FACE.caffemodel

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The license files can be found here https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning/blob/master/LICENSE.txt and here https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning/blob/master/NOTICE.txt

Ratings and reviews

Actually quite amazing. Simply train it with 20 pics of each person. Then activate the recognition training. And finally it recognises the face. The photos are put into the file system so that it's easy to edit/add your own. Very cool indeed. The I ly thing I need is some way to drive an external system from this - eg. output a get endpoint url per person. That would allow this system to drive home automation systems!

Its working fine when recognizing already registered person. But it doesnot work for unknown person who is not in database. For every new or unknown person it show the name located lastly in database.

Tried to scan a face on TV. Did not bring up anything.

What's new

- Switch from building Tensorflow from source to using the Jcenter library
- Included optimized_facenet model and changed default settings to use TensorFlow by default