Face Recognition

2.7
265 Rezensionen
100 Tsg.+
Downloads
Altersfreigabe
Jedes Alter
Screenshot
Screenshot
Screenshot
Screenshot
Screenshot
Screenshot
Screenshot
Screenshot

Über diese 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.
_______________________________________________________________

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
---------------------------------------------------------------------------------------------------------
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
Aktualisiert am
26.05.2017

Datensicherheit

Was die Sicherheit angeht, solltest du nachvollziehen, wie Entwickler deine Daten erheben und weitergeben. Die Datenschutz- und Sicherheitspraktiken können je nach Verwendung, Region und Alter des Nutzers variieren. Diese Informationen wurden vom Entwickler zur Verfügung gestellt und können jederzeit von ihm geändert werden.

Bewertungen und Rezensionen

2.7
255 Rezensionen

Neuigkeiten

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