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Face Recognition APK for Android

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Face Recognition is a free Android application designed for testing and developing face recognition algorithms. This utility serves as a powerful framework that allows users to evaluate various face recognition methods and generate training data for new algorithms. It includes a built-in image preprocessing algorithm compatible with any face recognition technique, enhancing the overall performance of the application. Additionally, users can utilize an image classification algorithm to optimize model performance, making it a versatile tool for developers and researchers alike.

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To utilize the TensorFlow model within the app, users must download the 'tensorflow_inception_graph.pb' file and place it in the specified directory on their device. The application requires the default settings for the preprocessing algorithm, ensuring a streamlined experience for users. Overall, Face Recognition combines essential features for effective algorithm testing and development in the realm of facial recognition technology.

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Icon of program: Face Recognition

Face Recognition APK for Android

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Full Specifications

GENERAL
Release
Latest update
Version
1.5.1
OPERATING SYSTEMS
Platform
Android
Operating System
Android 9.0
POPULARITY
Total Downloads
195
Downloads Last Week
0
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Developer’s Description

By sladomic
Face recognition can be used as a test framework for face recognition methods.
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/sladomic/Face-RecognitionAt the moment only armeabi-v7a devices and upwards are supported. For best experience in recognition mode rotate the device to left. For best performance use "Image Reshaping with Support Vector Machine" (0. 5 s / image). For best accuracy use the "VGG Face Descriptor" model (the performance is very bad though - 6. 5 s/ image). 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.
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Content created and reviewed by Softonic with information obtained from sladomic, using AI.

CNET's editorial team was not involved in the creation of this content. Opinions, analysis and reviews were not provided by CNET.