İndir Face Recognition - v1.5.1

Kitaplıklar ve Kısa Sunum kategorisindeki en havalı Uygulamalar olan Face Recognition - v1.5.1 veya V380s, SwipeYours, CommCare, V380 Pro, FTDA Extranet, Scandit Barcode Scanner Demo 'i duydunuz.

Ve tabii ki tüm oyunların veya uygulamaların tüm telefonlar için uyumlu olmadığını biliyorsunuz. Oyun veya uygulama bazen cihazınızda bulunmaz, sistemin sürümüne bağlıdır. Android işletim sistemi, ekran çözünürlüğü veya ülkeler Google Play'in erişime izin verdiği. Bu nedenle APKPanda, indirmeniz için Android APK dosyaları sağlar ve bu kısıtlamalara bağlı kalmaz.>
Face Recognition - v1.5.1 en son sürüm 1.5.1, yayın tarihi 2017-05-27 ve 54.4 MB boyutuna sahip.Qualeams tarafından geliştirilmiştir, Face Recognition - v1.5.1 en az Android sürümünü gerektirir Android 5.0+. Bu nedenle, gerekirse telefonunuzu güncellemelisiniz.

1000 indirme hakkında oldukça fazla yüklendi. İsterseniz Android cihazınıza tek tek indirilmiş veya yüklenmiş uygulamaları güncelleyebilirsiniz. Uygulamalarınızı güncellemek size en son özelliklere erişim izni verir ve uygulama güvenliğini ve kararlılığını artırır.

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