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Beskrivelse
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
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Nyheder
- Version: 1.5.1
- Opdateret:
- - Switch from building Tensorflow from source to using the Jcenter library
- Included optimized_facenet model and changed default settings to use TensorFlow by default
Prishistorie
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Yderligere oplysninger
- Version: 1.5.1
- Kategori:
Android Apps›Biblioteker og demoer - Operativsystem:
Android 5.0 - Størrelse:
21 Mb - Indholdsklassificering:
Everyone - Google Play Bedømmelse:
2.7(266) - Opdateret:
- Udgivelsesdato:
Kontaktpersoner
https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning-Test-Framework «Face Recognition». Platform: Android. Kategori: Biblioteker og demoer. Udvikler: «Qualeams». Første udgivelse: . Sidste opdatering: . Nuværende pris: gratis. Denne titel har endnu ikke modtaget vurderinger eller anmeldelser på AppAgg. Butik: Google Play. Butiksbedømmelse: 2.7. Stemmer: 266. AppAgg sporer prishistorik, vurderinger og brugerfeedback for «Face Recognition». Følg fremtidige rabatter og opdateringer via RSS. AppAgg hoster ikke applikationer eller distribuerer software. Alle varemærker, logoer og skærmbilleder tilhører deres respektive ejere.
Face Recognition Kort webadresse: Kopieret!- 🌟 Del
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