Dataset Card for Biwi Kinect Head Pose Database

Dataset Summary

The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device.It contains 15K images of 20 people with 6 females and 14 males where 4 people were recorded twice.

For each frame, there is :

• a depth image,
• a corresponding rgb image (both 640x480 pixels),
• annotation

The head pose range covers about +-75 degrees yaw and +-60 degrees pitch. The ground truth is the 3D location of the head and its rotation.

Data Processing

Example code for reading a compressed binary depth image file provided by the authors.

/*
 * Gabriele Fanelli
 *
 * fanelli@vision.ee.ethz.ch
 *
 * BIWI, ETHZ, 2011
 *
 * Part of the Biwi Kinect Head Pose Database
 *
 * Example code for reading a compressed binary depth image file.
 *
 * THE SOFTWARE IS PROVIDED “AS IS” AND THE PROVIDER GIVES NO EXPRESS OR IMPLIED WARRANTIES OF ANY KIND,
 * INCLUDING WITHOUT LIMITATION THE WARRANTIES OF FITNESS FOR ANY PARTICULAR PURPOSE AND NON-INFRINGEMENT.
 * IN NO EVENT SHALL THE PROVIDER BE HELD RESPONSIBLE FOR LOSS OR DAMAGE CAUSED BY THE USE OF THE SOFTWARE.
 *
 *
 */

#include <iostream>
#include <fstream>
#include <cstdlib>

int16_t* loadDepthImageCompressed( const char* fname ){

    //now read the depth image
    FILE* pFile = fopen(fname, "rb");
    if(!pFile){
        std::cerr << "could not open file " << fname << std::endl;
        return NULL;
    }

    int im_width = 0;
    int im_height = 0;
    bool success = true;

    success &= ( fread(&im_width,sizeof(int),1,pFile) == 1 ); // read width of depthmap
    success &= ( fread(&im_height,sizeof(int),1,pFile) == 1 ); // read height of depthmap

    int16_t* depth_img = new int16_t[im_width*im_height];
    
    int numempty;
    int numfull;
    int p = 0;

    while(p < im_width*im_height ){

        success &= ( fread( &numempty,sizeof(int),1,pFile) == 1 );

        for(int i = 0; i < numempty; i++)
            depth_img[ p + i ] = 0;

        success &= ( fread( &numfull,sizeof(int), 1, pFile) == 1 );
        success &= ( fread( &depth_img[ p + numempty ], sizeof(int16_t), numfull, pFile) == (unsigned int) numfull );
        p += numempty+numfull;

    }

    fclose(pFile);

    if(success)
        return depth_img;
    else{
        delete [] depth_img;
        return NULL;
    }
}

float* read_gt(const char* fname){

    //try to read in the ground truth from a binary file
    FILE* pFile = fopen(fname, "rb");
    if(!pFile){
        std::cerr << "could not open file " << fname << std::endl;
        return NULL;
    }
    
    float* data = new float[6];
    
    bool success = true;
    success &= ( fread( &data[0], sizeof(float), 6, pFile) == 6 );
    fclose(pFile);
    
    if(success)
        return data;
    else{
        delete [] data;
        return NULL;
    }

}

Supported Tasks and Leaderboards

Biwi Kinect Head Pose Database supports the following tasks :

• Head pose estimation
• Pose estimation
• Face verification

Languages

[Needs More Information]

Dataset Structure

Data Instances

A sample from the Biwi Kinect Head Pose dataset is provided below:

{
    'sequence_number': '12', 
    'subject_id': 'M06', 
    'rgb': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x7F53A6446C10>,.....],
    'rgb_cal': 
        {
            'intrisic_mat': [[517.679, 0.0, 320.0], [0.0, 517.679, 240.5], [0.0, 0.0, 1.0]],
            'extrinsic_mat': 
            {
                'rotation': [[0.999947, 0.00432361, 0.00929419], [-0.00446314, 0.999877, 0.0150443], [-0.009228, -0.015085, 0.999844]], 
                'translation': [-24.0198, 5.8896, -13.2308]
            }
        }
    'depth': ['../hpdb/12/frame_00003_depth.bin', .....],
    'depth_cal': 
        {
            'intrisic_mat': [[575.816, 0.0, 320.0], [0.0, 575.816, 240.0], [0.0, 0.0, 1.0]],
            'extrinsic_mat': 
            {
                'rotation': [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], 
                'translation': [0.0, 0.0, 0.0]
            }
        }
    'head_pose_gt': 
        {
            'center': [[43.4019, -30.7038, 906.864], [43.0202, -30.8683, 906.94], [43.0255, -30.5611, 906.659], .....],
            'rotation': [[[0.980639, 0.109899, 0.162077], [-0.11023, 0.993882, -0.00697376], [-0.161851, -0.011027, 0.986754]], ......]
        }
}

Data Fields

• sequence_number : This refers to the sequence number in the dataset. There are a total of 24 sequences.
• subject_id : This refers to the subjects in the dataset. There are a total of 20 people with 6 females and 14 males where 4 people were recorded twice.
• rgb : List of png frames containing the poses.
• rgb_cal: Contains calibration information for the color camera which includes intrinsic matrix, global rotation and translation.
• depth : List of depth frames for the poses.
• depth_cal: Contains calibration information for the depth camera which includes intrinsic matrix, global rotation and translation.
• head_pose_gt : Contains ground truth information, i.e., the location of the center of the head in 3D and the head rotation, encoded as a 3x3 rotation matrix.

Data Splits

All the data is contained in the training set.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device.

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

From Dataset's README :

The database contains 24 sequences acquired with a Kinect sensor. 20 people (some were recorded twice - 6 women and 14 men) were recorded while turning their heads, sitting in front of the sensor, at roughly one meter of distance.

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

From Dataset's README :

This database is made available for non-commercial use such as university research and education.

Citation Information

@article{fanelli_IJCV,
  author = {Fanelli, Gabriele and Dantone, Matthias and Gall, Juergen and Fossati, Andrea and Van Gool, Luc},
  title = {Random Forests for Real Time 3D Face Analysis},
  journal = {Int. J. Comput. Vision},
  year = {2013},
  month = {February},
  volume = {101},
  number = {3},
  pages = {437--458}
}

Contributions

Thanks to @dnaveenr for adding this dataset.