Face Recognition APK MOD (Premium Unlocked/ VIP/ PRO)

1.5.1 for Android, iOS and Windows

Download app Face Recognition MOD APK (Premium Unlocked/VIP/PRO) for Android, iOS and Windows - Free - latest-version.
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Developer
Qualeams
Version
1.5.1 for Android, iOS and Windows
Updated
May 26, 2017
Requirements
5.0
Size
54.41 MB
Get it on
Google Play

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Description

Face Recognition MOD APK 1.5.1 (Unlimited Everything / VIP/ PRO)

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.

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
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
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

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

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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

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