人脸识别4：Android InsightFace实现人脸识别Face Recognition(含源码)

目录

人脸识别4：Android InsightFace实现人脸识别Face Recognition(含源码)

1. 前言

2. 项目说明

（1）开发版本

（2）依赖库说明(OpenCV+OpenCL+base-utils+TNN)

（3）CMake配置

3. 人脸识别系统

（1）人脸识别的核心算法

（2）人脸检测和关键点检测

（3）人脸校准

（4）人脸特征提取

（5）人脸比对(1:1)

（6）人脸搜索(1:N)

（7）人脸识别优化建议

（8） 运行APP闪退：dlopen failed: library "libomp.so" not found

4. 人脸识别Android Demo效果

5. 人脸识别Python版本源码下载

6. 人脸识别C/C++版本源码下载

7. 人脸识别Android版本源码下载

1. 前言

这是项目《人脸识别Face Recognition》系列之《Android InsightFace实现人脸识别Face Recognition》；项目基于开源ArcFace(也称InsightFace)模型搭建一套完整的Android人脸识别系统（Face Recognition or Face Identification）；我们将开发一个简易的、可实时运行的人脸识别Android Demo。Android版本人脸识别模型推理支持CPU和GPU加速，在GPU(OpenCL)加速下，可以达到实时的人脸识别效果，非常适合在Linux开发板和Android系统开发板上部署。

整套人脸识别系统核心算法包含人脸检测和人脸关键点检测，人脸校准，人脸特征提取以及人脸比对(1:1)和人脸搜索(1:N)。本项目人脸识别系统可以达到目前商业级别的人脸识别准确率，在误识率(FAR)0.1%的情况下，可提供99.78%的通过率(TAR)；可以满足人脸比对，人脸签到、人脸门禁、人员信息查询、安防监控等人脸识别应用场景。

Android版本人脸检测和人脸识别效果:

【尊重原创，转载请注明出处】https://blog.csdn.net/guyuealian/article/details/130600600

更多项目《人脸识别Face Recognition》系列文章请参考：

  1. 人脸识别1：人脸识别数据集https://blog.csdn.net/guyuealian/article/details/130600545
  2. 人脸识别2：InsightFace实现人脸识别Face Recognition(含源码下载)人脸识别2：InsightFace实现人脸识别Face Recognition(含源码下载)_insightface 识别_AI吃大瓜的博客-CSDN博客
  3. 人脸识别3：C/C++ InsightFace实现人脸识别Face Recognition(含源码)人脸识别3：C/C++ InsightFace实现人脸识别Face Recognition(含源码)_AI吃大瓜的博客-CSDN博客
  4. 人脸识别4：Android InsightFace实现人脸识别Face Recognition(含源码)https://blog.csdn.net/guyuealian/article/details/130600600

2. 项目说明

项目依赖库主要有OpenCV，base-utils，TNN和OpenCL(用于加速)，项目源码已经包含了相关依赖库，且都已经配置好，无需安装；使用Android Studio直接build即可运行App Demo ；

（1）开发版本

Android SDK,NDK，Jave等版本信息，请参考：

（2）依赖库说明(OpenCV+OpenCL+base-utils+TNN)

项目模型推理采用TNN部署框架（支持多线程CPU和GPU加速推理）；图像处理采用OpenCV库，模型加速采用OpenCL，在普通手机设备即可达到实时处理。项目Android源码已经配置好OpenCV+OpenCL+base-utils+TNN依赖库，无需重新配置，Android Studio直接build，即可运行。

  • OpenCV：图像处理（如读取图片，图像裁剪等）都需要使用OpenCV库进行处理（无需安装，项目已经配置了）
  • OpenCL：OpenCL用于模型GPU加速，若不使用OpenCL进行模型推理加速，纯C++推理模型，速度会特别特别慢（无需安装，项目已经配置了）
  • base-utils：是个人开发常用的C++库，集成了C/C++ OpenCV等常用的算法：https://github.com/PanJinquan/base-utils （无需安装，项目已经配置了）
  • TNN：模型推理框架：https://github.com/Tencent/TNN （无需安装，项目已经配置了）

（3）CMake配置

人脸识别核心算法均采用C++实现，上层Java应用使用JNI调用底层算法，CMake最低版本3.5.0，这是CMakeLists.txt，其中主要配置OpenCV+OpenCL+base-utils+TNN这四个库：

cmake_minimum_required(VERSION 3.5.0)
project("TNN")
add_compile_options(-fPIC) # fix Bug: can not be used when making a shared object
#set(CMAKE_BUILD_TYPE Release)
#set(CMAKE_BUILD_TYPE "Release" CACHE STRING "Build type (default Debug)" FORCE)
#set(CMAKE_CXX_FLAGS "-Wall -std=c++11 -pthread")
set(CMAKE_BUILD_TYPE "Release" CACHE STRING "set build type to release" FORCE)# opencv set
# copy `OpenCV-android-sdk/sdk` to `3rdparty/opencv/`
set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/3rdparty/opencv/sdk/native/jni)
find_package(OpenCV REQUIRED)
include_directories(${CMAKE_SOURCE_DIR}/3rdparty/opencv/sdk/native/jni/include)# base_utils
set(BASE_ROOT 3rdparty/base-utils) # 设置base-utils所在的根目录
add_subdirectory(${BASE_ROOT}/base_utils/ base_build) # 添加子目录到build中
include_directories(${BASE_ROOT}/base_utils/include)
include_directories(${BASE_ROOT}/base_utils/src)
MESSAGE(STATUS "BASE_ROOT = ${BASE_ROOT}")# TNN set
# Creates and names a library, sets it as either STATIC
# or SHARED, and provides the relative paths to its source code.
# You can define multiple libraries, and CMake builds it for you.
# Gradle automatically packages shared libraries with your APK.
# build for platform
# set(TNN_BUILD_SHARED OFF CACHE BOOL "" FORCE)
if (CMAKE_SYSTEM_NAME MATCHES "Android")set(TNN_OPENCL_ENABLE ON CACHE BOOL "" FORCE)set(TNN_ARM_ENABLE ON CACHE BOOL "" FORCE)set(TNN_BUILD_SHARED OFF CACHE BOOL "" FORCE)set(TNN_OPENMP_ENABLE ON CACHE BOOL "" FORCE)  # Multi-Thread#set(TNN_HUAWEI_NPU_ENABLE OFF CACHE BOOL "" FORCE)add_definitions(-DTNN_OPENCL_ENABLE)           # for OpenCL GPUadd_definitions(-DTNN_ARM_ENABLE)              # for Android CPUadd_definitions(-DDEBUG_ANDROID_ON)            # for Android Logadd_definitions(-DPLATFORM_ANDROID)
elseif (CMAKE_SYSTEM_NAME MATCHES "Linux")set(TNN_OPENCL_ENABLE ON CACHE BOOL "" FORCE)set(TNN_CPU_ENABLE ON CACHE BOOL "" FORCE)set(TNN_X86_ENABLE OFF CACHE BOOL "" FORCE)set(TNN_QUANTIZATION_ENABLE OFF CACHE BOOL "" FORCE)set(TNN_OPENMP_ENABLE ON CACHE BOOL "" FORCE)  # Multi-Threadadd_definitions(-DTNN_OPENCL_ENABLE)           # for OpenCL GPUadd_definitions(-DDEBUG_ON)                    # for WIN/Linux Logadd_definitions(-DDEBUG_LOG_ON)                # for WIN/Linux Logadd_definitions(-DDEBUG_IMSHOW_OFF)            # for OpenCV showadd_definitions(-DPLATFORM_LINUX)
elseif (CMAKE_SYSTEM_NAME MATCHES "Windows")set(TNN_OPENCL_ENABLE ON CACHE BOOL "" FORCE)set(TNN_CPU_ENABLE ON CACHE BOOL "" FORCE)set(TNN_X86_ENABLE ON CACHE BOOL "" FORCE)set(TNN_QUANTIZATION_ENABLE OFF CACHE BOOL "" FORCE)set(TNN_OPENMP_ENABLE ON CACHE BOOL "" FORCE)  # Multi-Threadadd_definitions(-DTNN_OPENCL_ENABLE)           # for OpenCL GPUadd_definitions(-DDEBUG_ON)                    # for WIN/Linux Logadd_definitions(-DDEBUG_LOG_ON)                # for WIN/Linux Logadd_definitions(-DDEBUG_IMSHOW_OFF)            # for OpenCV showadd_definitions(-DPLATFORM_WINDOWS)
endif ()
set(TNN_ROOT 3rdparty/TNN)
include_directories(${TNN_ROOT}/include)
include_directories(${TNN_ROOT}/third_party/opencl/include)
add_subdirectory(${TNN_ROOT}) # 添加外部项目文件夹
set(TNN -Wl,--whole-archive TNN -Wl,--no-whole-archive)# set TNN library
MESSAGE(STATUS "TNN_ROOT = ${TNN_ROOT}")# NPU Set
if (TNN_HUAWEI_NPU_ENABLE)add_library(hiaiSHAREDIMPORTED)set_target_properties(hiaiPROPERTIESIMPORTED_LOCATION${CMAKE_SOURCE_DIR}/src/main/jni/thirdparty/hiai_ddk/${ANDROID_ABI}/libhiai.so)add_library(hiai_irSHAREDIMPORTED)set_target_properties(hiai_irPROPERTIESIMPORTED_LOCATION${CMAKE_SOURCE_DIR}/src/main/jni/thirdparty/hiai_ddk/${ANDROID_ABI}/libhiai_ir.so)add_library(hiai_ir_buildSHAREDIMPORTED)set_target_properties(hiai_ir_buildPROPERTIESIMPORTED_LOCATION${CMAKE_SOURCE_DIR}/src/main/jni/thirdparty/hiai_ddk/${ANDROID_ABI}/libhiai_ir_build.so)endif ()find_library( # Sets the name of the path variable.log-lib# Specifies the name of the NDK library that# you want CMake to locate.log)# Specifies libraries CMake should link to your target library. You
# can link multiple libraries, such as libraries you define in the
# build script, prebuilt third-party libraries, or system libraries.# dmcv库
include_directories(src)set(SRC_LISTsrc/face_alignment.cppsrc/face_recognizer.cppsrc/face_feature.cppsrc/object_detection.cppsrc/Interpreter.cpp)MESSAGE(STATUS "DIR_SRCS = ${SRC_LIST}")# JNI接口库
add_library(tnn_wrapper SHARED jni_interface.cpp ${SRC_LIST})
target_link_libraries( # Specifies the target library.tnn_wrapper-ljnigraphics# Links the target library to the log library# included in the NDK.${log-lib}${android-lib}${jnigraphics-lib}${TNN}${OpenCV_LIBS}base_utils)if (TNN_HUAWEI_NPU_ENABLE)target_link_libraries( # Specifies the target library.tnn_wrapper hiai hiai_ir hiai_ir_build)
endif ()

3. 人脸识别系统

人脸识别主要包含人脸比对(1:1)和人脸搜索(1:N)两大功能，涉及的核心算法主要包含：人脸检测和人脸关键点检测，人脸校准，人脸特征提取以及人脸比对(1:1)和人脸搜索(1:N)；当然，实际业务中，可能还会增加人脸质量检测以及活体识别等算法，碍于篇幅，后续再分享活体识别算法。

下图给出本项目人脸识别系统算法实现架构流程图：

（1）人脸识别的核心算法

项目实现了人脸识别的核心算法，包含人脸检测和人脸关键点检测，人脸校准，人脸特征提取以及人脸比对(1:1)和人脸搜索(1:N)等功能，可以参文件(src/main/java/com/cv/tnn/model/FaceRecognizer.java)，实现人脸识别的基本功能

package com.cv.tnn.model;import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.util.Log;import java.io.File;
import java.util.List;public class FaceRecognizer {private static final String TAG = "FaceRecognizer";public FaceRecognizer(String det_model, String rec_model, String root, String database, int model_type, int num_thread, boolean useGPU) {Log.w(TAG, "det_model =" + det_model);Log.w(TAG, "rec_model =" + rec_model);Log.w(TAG, "root      =" + root);Log.w(TAG, "database  =" + database);Log.w(TAG, "model_type=" + model_type);Log.w(TAG, "num_thread=" + String.valueOf(num_thread));Log.w(TAG, "useGPU    =" + String.valueOf(useGPU));FileChooseUtil.createFolder(root);Detector.init(det_model, rec_model, root, database, model_type, num_thread, useGPU);}/**** 进行人脸检测* @param bitmap 输入图像* @param det_conf_thresh 人脸检测置信度阈值，范围0.~1.0* @param det_iou_thresh  人脸检测IOU阈值，范围0.~1.0* @return*/public FrameInfo[] detectFace(Bitmap bitmap, float det_conf_thresh, float det_iou_thresh) {FrameInfo[] result = null;result = Detector.detectFace(bitmap, det_conf_thresh, det_iou_thresh);return result;}/**** 通过导入文件夹路径，进行批量注册人脸，* 请将图片按照[ID-XXXX.jpg]命名,如:张三-image.jpg* @param folder 文件夹路径* @param det_conf_thresh 人脸检测置信度阈值，范围0.~1.0* @param det_iou_thresh  人脸检测IOU阈值，范围0.~1.0*/public void registerFromFolder(String folder, float det_conf_thresh, float det_iou_thresh) {Log.w(TAG, "database folder=" + folder);List<String> image_list = FileChooseUtil.getImagePathFromSD(folder);for (int i = 0; i < image_list.size(); i++) {String image_file = image_list.get(i);FrameInfo[] result = registerFromFile(image_file, det_conf_thresh, det_iou_thresh);}}/**** 通过导入图片的路径，进行注册* 请将图片按照[ID-XXXX.jpg]命名,如:张三-image.jpg* @param image_file 图片的路径* @param det_conf_thresh 人脸检测置信度阈值，范围0.~1.0* @param det_iou_thresh  人脸检测IOU阈值，范围0.~1.0* @return*/public FrameInfo[] registerFromFile(String image_file, float det_conf_thresh, float det_iou_thresh) {String[] paths = image_file.split(File.separator);String basename = paths[paths.length - 1];String face_id = basename.split("-")[0];if (face_id.length() == basename.length()) {Log.w(TAG, "file=" + image_file + ",图片名称不合法，请将图片按照[ID-XXXX.jpg]命名,如:张三-image.jpg");}Bitmap bitmap = BitmapFactory.decodeFile(image_file);FrameInfo[] result = registerFromBitmap(face_id, bitmap, det_conf_thresh, det_iou_thresh);if (result.length > 0) {Log.w(TAG, "file=" + image_file + ",注册人脸成功:ID=" + face_id);} else {Log.w(TAG, "file=" + image_file + ",注册人脸失败:ID=" + face_id);}return result;}/**** 通过导入Bitmap图像，进行注册* @param face_id 人脸ID* @param bitmap  Bitmap图像* @param det_conf_thresh 人脸检测置信度阈值，范围0.~1.0* @param det_iou_thresh  人脸检测IOU阈值，范围0.~1.0* @return*/public FrameInfo[] registerFromBitmap(String face_id, Bitmap bitmap, float det_conf_thresh, float det_iou_thresh) {FrameInfo[] result = null;//bitmap = bitmap.copy(Bitmap.Config.ARGB_8888, true);result = Detector.registerFace(face_id, bitmap, det_conf_thresh, det_iou_thresh);return result;}/**** 人脸识别1:N人脸搜索* @param bitmap   Bitmap图像* @param max_face 最大人脸个数，默认为-1，表示全部人脸* @param det_conf_thresh 人脸检测置信度阈值，范围0.~1.0* @param det_iou_thresh  人脸检测IOU阈值，范围0.~1.0* @param rec_conf_thresh 人脸识别相似度阈值，范围0.~1.0* @return*/public FrameInfo[] detectSearch(Bitmap bitmap, int max_face, float det_conf_thresh, float det_iou_thresh, float rec_conf_thresh) {FrameInfo[] result = null;result = Detector.detectSearch(bitmap, max_face, det_conf_thresh, det_iou_thresh, rec_conf_thresh);return result;}/**** 人脸识别1:1人脸验证，比较两张人脸的相似性* @param bitmap1 输入第1张人脸图像* @param bitmap2 输入第2张人脸图像* @param det_conf_thresh 人脸检测置信度阈值，范围0.~1.0* @param det_iou_thresh  人脸检测IOU阈值，范围0.~1.0* @return*/public float compareFace(Bitmap bitmap1, Bitmap bitmap2, float det_conf_thresh, float det_iou_thresh) {return Detector.compareFace(bitmap1, bitmap2, det_conf_thresh, det_iou_thresh);}/**** 提取人脸特征(先进行检测，再提取人脸特征)* @param bitmap 输入人脸图像* @param max_face 最大人脸个数，默认为-1，表示全部人脸* @param det_conf_thresh 人脸检测置信度阈值，范围0.~1.0* @param det_iou_thresh  人脸检测IOU阈值，范围0.~1.0* @return*/public FrameInfo[] getFeature(Bitmap bitmap, int max_face, float det_conf_thresh, float det_iou_thresh) {FrameInfo[] result = null;result = Detector.getFeature(bitmap, max_face, det_conf_thresh, det_iou_thresh);return result;}/**** 清空人脸数据库(会删除所有已经注册的人脸数据，谨慎操作)*/public void clearDatabase() {Detector.clearDatabase();}
}

（2）人脸检测和关键点检测

人脸检测的方法比较多，项目Python版本人脸识别提供两种人脸检测方法：一种是基于MTCNN的通用人脸检测模型，另一种是轻量化的、快速的RFB人脸检测模型；这个两个模型都能实现人脸检测，并同时预测人脸的五个关键点(Landmark)。C/C++和Android版本只提供RFB人脸检测和关键点检测模型。

模型 Paper 源码 说明
MTCNN Paper Link
  • 支持人脸检测和人脸关键点检测(5个点)
  • 通用场景人脸检测，计算量较大，适合PC服务器部署
RFB Paper Link
  • 支持人脸检测和人脸关键点检测(5个点)
  • 轻量级人脸检测，适合简单场景人脸检测，计算量较小，适合嵌入式，开发板，Android等终端部署