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Download Face Recognition - v1.5.1
Package Name | ch.zhaw.facerecognition |
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Category | APPS, Software & Demos |
Latest Version | 1.5.1 |
Get it On | |
Update | December 18, 2019 (5 years ago) |
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Face Recognition - v1.5.1 wurde von Qualeams veröffentlicht und ist eine der besten kostenlosen und besten Mobiltelefonanwendungen, die derzeit verfügbar sind. Sie befindet sich in der Kategorie Software & Demos des App Store.
Das Mindestbetriebssystem für Face Recognition - v1.5.1 ist Android 5.0+ und höher. Sie müssen Ihr Telefon also aktualisieren, wenn Sie dies noch nicht getan haben.
Bei APKDroid erhalten Sie Face Recognition - v1.5.1 APK zum kostenlosen Download. Die neueste Version ist 1.5.1, Veröffentlichungsdatum 2017-05-27, die Dateigröße ist 54.4 MB.Laut Statistiken aus dem Google Play Store gibt es ungefähr 1000 Downloads. Apps, die einzeln auf Android heruntergeladen oder installiert wurden, können auf Wunsch aktualisiert werden. Aktualisieren Sie auch Ihre Apps. Gewährt Ihnen Zugriff auf die neuesten Funktionen und verbessert die Sicherheit und Stabilität der App. Genießen Sie es jetzt !!!
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)- ResizeYou 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 KNNThe manual can be found here https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning/blob/master/USER%20MANUAL.mdAt 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.zipThen 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: 224Image mean: 128Output size: 1024Input layer: inputOutput layer: avgpool0Model 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=0Caution: 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: 224Image mean: 128Output size: 4096Input layer: PlaceholderOutput layer: fc7/fc7Model 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.gzCaution: 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, 123Output layer: fc7Model file: VGG_FACE_deploy.prototxtWeights 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
- 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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