Scarica Face Recognition - v1.5.1

Scarica e condividi Face Recognition - v1.5.1, uno degli elementi in primo piano App nella categoria Librerie e demo.
Inoltre, qualche altro App che puoi scaricare come VSCAM, Guide For AnTuTu Benchmark, V380 Pro, Game Booster Free Power GFX Lag Fix, FTDA Extranet, Pydroid repository plugin. Se sei soddisfatto di Face Recognition - v1.5.1.

Rilasciata da Qualeams, Face Recognition - v1.5.1 è una delle migliori applicazioni gratuite per telefoni cellulari attualmente disponibili. Si trova nella categoria Librerie e demo dell'app store.

Il sistema operativo minimo per Face Recognition - v1.5.1 è Android 5.0+ e versioni successive. Quindi devi aggiornare il telefono se non l'hai già fatto.

Su APKDroid, riceverai Face Recognition - v1.5.1 download gratuito di APK, con l'ultima versione 1.5.1, data di pubblicazione 2017-05-27, la dimensione del file è 54.4 MB.Secondo le statistiche del Google Play Store, ci sono circa 1000 download. Le app scaricate o installate singolarmente su Android possono essere aggiornate se lo desideri. Aggiorna anche le tue app. Ti garantisce l'accesso alle funzioni più recenti e alla sicurezza e stabilità dell'app. Divertiti subito !!!

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