Face Recognition 1.0 Icon

Face Recognition

Qualeams Libraries & Demo
2.7
22 Ratings
108K+
Downloads
1.0
version
Jun 20, 2016
release date
13.2 MB
file size
Free
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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

About Face Recognition Android 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

Other Information:

Package Name:
Requires Android:
Android 5.0+
Other Sources:

Download

This release of Face Recognition Android App available in 5 variants. Please select the variant to download. Please read our FAQ to find out which variant is suitable for your Android device based on Screen DPI and Processor Architecture.

Variant
13
(Jun 20, 2016)
Architecture
armeabi-v7a
Minimum OS
Android 5.0+
Screen DPI
160-640dpi
Variant
12
(Jun 19, 2016)
Architecture
armeabi-v7a
Minimum OS
Android 5.0+
Screen DPI
160-640dpi
Variant
11
(Jun 07, 2016)
Architecture
armeabi-v7a
Minimum OS
Android 5.0+
Screen DPI
160-640dpi
Variant
10
(Jun 06, 2016)
Architecture
armeabi-v7a
Minimum OS
Android 5.0+
Screen DPI
160-640dpi
Variant
9
(May 29, 2016)
Architecture
armeabi-v7a
Minimum OS
Android 5.0+
Screen DPI
160-640dpi
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