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Package Summary

Continuous integration Documented

A ROS package for face recognition in video stream. Face recognition is performed using Eigenfaces (also called "Principal Component Analysis" or PCA) by utilizing the c++ source code provided by Shervin Emami (http://www.shervinemami.info/faceRecognition.html)

  • Maintainer status: maintained
  • Maintainer: Pouyan Ziafati (Author) <pouyan.ziafati AT uni DOT lu>
  • Author: Pouyan Ziafati <pouyan.ziafati AT uni DOT lu>
  • License: Attribution-NonCommercial 3.0
  • Source: git https://github.com/procrob/face_recognition.git (branch: catkin)
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The ProcRob face_recognition package can be downloaded from https://github.com/procrob/face_recognition . For usage guide, please see below. For details of the face recognition algorithm used, please see http://www.shervinemami.info/faceRecognition.html .

Installation

This instalation process is for rosbuild (ROS Fuerte or earlier version) Assuming that your rosbuild workspace (rosws) is under ~/rosbuild_ws, if not replace ~/rosbuild_ws with appropriate location. It also assumes you're running Bash shell, if you're running Zsh, source appropriate setup.zsh file.

Execute:

$ cd ~/rosbuild_ws
$ rosws set face_recognition --git git://github.com/procrob/procrob_functional.git
$ rosws update

In new terminal

$ rosmake face_recognition

How the face recognition works:

Training images are stored in the data directory.
Training images are listed in the train.text file.
The train.txt follows a specific format which is best understood by looking at the example train.txt file provided in the package. Note that person numbers start from 1, and spaces or special characters are not allowed in persons' names).
The program trains from the training examples listed in the train.txt and create an Eigenfaces database which is stored in the facedata.xml file.
Face detection is performed using a haarcascade classifier (haarcascade_frontalface_alt.xml).
The data folder, and train.txt, facedata.xml and haarcascade_frontalface_alt.xml files should be placed in the program's working directory (i.e. the directory from which you execute the program).

When the face_recognition program starts:
If facedata.xml exists, the Eigenfaces database is loaded from facedata.xml.
If facedata.xml does not exist, the program tries to train and create Eigenfaces database from the training images listed in train.txt, if any.
Regardless of if the Eigenfaces database is loaded/created at start up or not, you can always add training images directly from the video stream and then update the Eigenfaces database by (re)training.
Note: when the program (re)trains, the content of facedata.xml is disregarded and the program trains only based on the training images listed in train.txt.

Fserver

Fserver is a ROS node that provides a simple actionlib server interface for performing different face recognition functionalities in video stream.

To start the Fserver node

$ roscd face_recognition
$ rosrun face_recognition Fserver

FaceRecognitionGoal message

This message includes 2 fields:

  • int order_id

  • string order_argument

The FaceRecognitionGoal message has 2 fields: an order_id which is an integer specifying a goal and an order_argument which is a string used to specify an argument for the goal if necessary. The face recognition actionlib server (i.e. Fserver) accepts 5 different goals:

  • order_id = 0

recognise_once: Goal is to acknowledge the first face recognized in the video stream. When the first face is recognized with a confidence value higher than the desirable confidence value threshold, the name of the recognized person and its confidence value are sent back to the client as result.

  • order_id = 1

recognise_continuous: Goal is to continuously recognise faces in the video stream. For every face recognized with a confidence value higher than the desirable confidence value threshold, the name of the recognized person and its confidence value are sent back to the client as feedback. This goal is persuaded for infinite time until it is cancelled or preempted by another goal.

  • order_id = 2 and order_argument = 'person_name'

add_face_images: Goal is to acquire training images for a NEW person. The video stream is processed for detecting a face which is saved and used as a training image for the new person. This process is continued until the desired number of training images for the new person is acquired. The name of the new person is provided as order_argument. The acquired images are stored in the 'data' folder and are added to the list of training images in train.txt

  • order_id = 3

train: Goal is to (re)train the Eigenfaces database from the training images listed in the 'train.txt'.

  • order_id = 4

exit: Goal is to exit the program.

Subscribed Topic:

  • /camera/image_raw (standard ROS image transport)

A video stream

Parameters:

  • confidence_value (double, default = 0.88)

A face recognized with confidence value higher than the "confidence_value" threshold is accepted as valid.

  • show_screen_flag (boolean, default = true)

If output screen is shown.

  • add_face_number (int, default = 25)

A parameter for the 'add_face_images' goal (order_id = 2) which determines the number of training images for a new person to be acquired from the video stream

Fclient

Fclient is a ROS node that implements an actionlib client example for the face_recognition simple actionlib server (i.e. 'Fserver'). 'Fclient' is provided for demonstration and testing purposes.

Subscribed topics

  • fr_order (face_recognition/FRClientGoal)

Each FRClientGoal message has an order_id and an order_argument which specify a goal to be executed by the Fserver. After receiving a message, Fclient sends the corresponding goal to the Fserver. By registering relevant call back functions, Fclient receives feedback and result information from the execution of goals in the Fserver and prints such information on its terminal.

Tutorial

(You have to install gscam to do this tutorial) An exercise of using Fserver and Fclient:

  • Run roscore

$ roscore
  • In a separate terminal publish a video stream on topic /camera/image_raw. If you have the gscam package installed and configured, you can use it to publish images from your web cam as follows:

$ roscd gscam/bin
$ rosrun gscam gscam /gscam/image_raw:=/camera/image_raw
  • In separate terminals run the face recognition server and client as follows:

$ rosrun face_recognition Fserver
$ rosrun face_recognition Fclient
  • In another terminal publish following messages on topic /fr_order to test different face recognition functionalities.
  • After each command notice the output of Fserver and Fclient.

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 2 "your_name"
  • To acquire training images for your face: you should try to appear in the video stream!

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 3 "none"
  • To retrain and update the database, so that you can be recognized

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 1 "none" 
  • To recognise faces continuously. This would not stop until you preempt or cancel the goal. So lets preempt it by sending the next goal.

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 2 "your_friend's_name"
  • To add training images for a new person

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 0 "none"
  • To recognize once a face.

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 1 "none" 
  • To recognize continuously

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 4 "none" 
  • To exit

This instalation process is for catkin (ROS Groovy or newer version) Assuming that your catkin workspace is under ~/catkin_ws, if not replace ~/catkin_ws with appropriate location. It also assumes you're running Bash shell, if you're running Zsh, source appropriate setup.zsh file.

Execute:

$ cd ~/catkin_ws/src
$ git clone https://github.com/procrob/procrob_functional.git --branch catkin
$ cd ~/catkin_ws
$ catkin_make
$ source ~/catkin_ws/devel/setup.bash

How the face recognition works:

Training images are stored in the data directory.
Training images are listed in the train.text file.
The 'train.txt' follows a specific format which is best understood by looking at the example train.txt file provided in the package. Note that person numbers start from 1, and spaces or special characters are not allowed in persons' names).
The program trains from the training examples listed in the train.txt and create an Eigenfaces database which is stored in the facedata.xml file.
Face detection is performed using a haarcascade classifier (haarcascade_frontalface_alt.xml).
The data folder, and train.txt, facedata.xml and haarcascade_frontalface_alt.xml files should be placed in the program's working directory (i.e. the directory from which you execute the program).

When the face_recognition program starts:
If facedata.xml exists, the Eigenfaces database is loaded from facedata.xml.
If facedata.xml does not exist, the program tries to train and create Eigenfaces database from the training images listed in train.txt, if any.
Regardless of if the Eigenfaces database is loaded/created at start up or not, you can always add training images directly from the video stream and then update the Eigenfaces database by (re)training.
Note: when the program (re)trains, the content of facedata.xml is disregarded and the program trains only based on the training images listed in train.txt.

Fserver

Fserver is a ROS node that provides a simple actionlib server interface for performing different face recognition functionalities in video stream.

To start the Fserver node

$ roscd face_recognition
$ rosrun face_recognition Fserver

FaceRecognitionGoal message

This message includes 2 fields:

  • int order_id

  • string order_argument

The FaceRecognitionGoal message has 2 fields: an order_id which is an integer specifying a goal and an order_argument which is a string used to specify an argument for the goal if necessary. The face recognition actionlib server (i.e. Fserver) accepts 5 different goals:

  • order_id = 0

recognise_once: Goal is to acknowledge the first face recognized in the video stream. When the first face is recognized with a confidence value higher than the desirable confidence value threshold, the name of the recognized person and its confidence value are sent back to the client as result.

  • order_id = 1

recognise_continuous: Goal is to continuously recognise faces in the video stream. For every face recognized with a confidence value higher than the desirable confidence value threshold, the name of the recognized person and its confidence value are sent back to the client as feedback. This goal is persuaded for infinite time until it is cancelled or preempted by another goal.

  • order_id = 2 and order_argument = 'person_name'

add_face_images: Goal is to acquire training images for a NEW person. The video stream is processed for detecting a face which is saved and used as a training image for the new person. This process is continued until the desired number of training images for the new person is acquired. The name of the new person is provided as order_argument. The acquired images are stored in the 'data' folder and are added to the list of training images in train.txt

  • order_id = 3

train: Goal is to (re)train the Eigenfaces database from the training images listed in the 'train.txt'.

  • order_id = 4

exit: Goal is to exit the program.

Subscribed Topic:

  • /camera/image_raw (standard ROS image transport)

A video stream

Parameters:

  • confidence_value (double, default = 0.88)

A face recognized with confidence value higher than the "confidence_value" threshold is accepted as valid.

  • show_screen_flag (boolean, default = true)

If output screen is shown.

  • add_face_number (int, default = 25)

A parameter for the 'add_face_images' goal (order_id = 2) which determines the number of training images for a new person to be acquired from the video stream

Fclient

Fclient is a ROS node that implements an actionlib client example for the face_recognition simple actionlib server (i.e. 'Fserver'). 'Fclient' is provided for demonstration and testing purposes.

Subscribed topics

  • fr_order (face_recognition/FRClientGoal)

Each FRClientGoal message has an order_id and an order_argument which specify a goal to be executed by the Fserver. After receiving a message, Fclient sends the corresponding goal to the Fserver. By registering relevant call back functions, Fclient receives feedback and result information from the execution of goals in the Fserver and prints such information on its terminal.

Tutorial

An exercise of using Fserver and Fclient:

  • Run roscore

$ roscore
  • In separate terminal publish a video stream on topic /camera/image_raw.

  • For example you can use usb_cam to publish images from your web cam as follows:

  • Install http://wiki.ros.org/usb_cam package

  • Run

$ rosrun usb_cam usb_cam_node usb_cam_node/image_raw:=camera/image_raw _image_height:=<usb_cam_height> _image_width:=<usb_cam_width>
  • In separate terminals run the face recognition server and client as follows:

$ rosrun face_recognition Fserver
$ rosrun face_recognition Fclient
  • In another terminal publish following messages on topic /fr_order to test different face recognition functionalities.
  • After each command notice the output of Fserver and Fclient.

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 2 "your_name"
  • To acquire training images for your face: you should try to appear in the video stream!

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 3 "none"
  • To retrain and update the database, so that you can be recognized

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 1 "none" 
  • To recognise faces continuously. This would not stop until you preempt or cancel the goal. So lets preempt it by sending the next goal.

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 2 "your_friend's_name"
  • To add training images for a new person

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 0 "none"
  • To recognize once a face.

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 1 "none" 
  • To recognize continuously

$ rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 4 "none" 
  • To exit

Wiki: face_recognition (last edited 2015-08-31 09:31:55 by PouyanZiafati)