OAID/FaceRecognition

Stars
148
Rank 249,983 (Top 5 %)
Language
C++
Created about 7 years ago
Updated almost 7 years ago

OAID/FaceRecognition

OAID

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

This is an implematation project of face detection and recognition. The face detection using MTCNN algorithm, and recognition using LightenenCNN algorithm.

FaceRecognition

FaceRecognition is an implementation project of face detection and recognition. The face detection using MTCNN algorithm, and recognition using LightenedCNN algorithm.

The release version is 0.1.3, is based on ROCK960 Platform, target OS is Ubuntu 16.04.

MTCNN is a deep cascaded multi-task framework to boost up face detection performance. See also OAID/FaceDetection.
Lightened CNN is a light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels. See also LightenedCNN.

Documents

Release History

Version 0.1.3 - 2018-2-1

Fix bugs in accuracy test. Add FaceRecognition test control.

Version 0.1.2 - 2018-1-26

Add support to test MTCNN and lightenedCNN. Update log and time control.

Version 0.1.1 - 2017-12-29

Add support to set the max number of faces.

Version 0.1.0 - 2017-10-23

Initial version supports face register, face detection, and face recognization.

Issue Report

Encounter any issue, please report on issue report. Issue report should contain the following information:

The exact description of the steps that are needed to reproduce the issue.
The exact description of what happens and what you think is wrong.

Tengine

Tengine is a lite, high performance, modular inference engine for embedded device

TengineKit

TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes & Hand Detection & Hand Landmarks & Body Detection & Body Landmarks & Iris Landmarks & Yolov5 SDK On Mobile.

AutoKernel

AutoKernel 是一个简单易用，低门槛的自动算子优化工具，提高深度学习算法部署效率。

FaceDetection

C++ project to implement MTCNN, a perfect face detect algorithm, on different DL frameworks. The most popular frameworks: caffe/mxnet/tensorflow, are all suppported now

Caffe-HRT

Heterogeneous Run Time version of Caffe. Added heterogeneous capabilities to the Caffe, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original Caffe architecture which users deploy their applications seamlessly.

YSQfastfd

A fast binary library for face detection and face landmark detection in images. No float point operations, especially suit for low cost Arm CPUs, The highest accuracy on FDDB among non deep learning methods

Tengine-Convert-Tools

Tengine Convert Tool supports converting multi framworks' models into tmfile that suitable for Tengine-Lite AI framework.

TengineFactory

Algorithm acceleration landing framework, let you complete the development of algorithm at low cost.eg: Facedetect, FaceLandmark..

TengineGst

TengineGst is a streaming media analytics framework, based on GStreamer multimedia framework, for creating varied complex media analytics pipelines. It ensures pipeline interoperability and provides optimized media, and inference operations using Tengine Toolkit Inference Engine backend, across varied architecture - CPU, iGPU and VPU.

MXNet-HRT

Heterogeneous Run Time version of MXNet. Added heterogeneous capabilities to the MXNet, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original MXNet architecture which users deploy their applications seamlessly.

cortex-m-kws

Cortex M KWS example with Tengine Lite.

Tengine-app

This is a repository for applications powered by Tengine. Please feel free to try them on your device

ObjectTracker

This object Tracker algorithm is a TLD(Long-term tracker) tracker base on KCF or DSST.

SpeechRecognition

A local auto speech recognition project based on Kaldi and ALSA.

TensorFlow-HRT

Heterogeneous Run Time version of TensorFlow. Added heterogeneous capabilities to the TensorFlow, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original TensorFlow architecture which users deploy their applications seamlessly.

CVGesture

It implements detection and recognition to different hand gestures, based on OpenCV 3.3.0 (Open Source Computer Vision Library)

TengineInferPipe

FaceRecognition2

FaceRecognition2 is an implementation project of face detection and recognition. The face detection using MTCNN algorithm, and recognition using LightenedCNN algorithm.

AndroidFacedemo

An face recogniztion detect project on android based on CaffeOnACL

AndroidCVDemo

An CV demo in Android, include Gesture recognition and Object traking

autokernel-docs-en

autokernel documentation (english version)

autokernel-docs-cn

autokernel documentation