Add Biwi Kinect Head Pose dataset.
This PR adds the Biwi Kinect Head Pose dataset.

Dataset Request : Add Biwi Kinect Head Pose Database [#3822](https://github.com/huggingface/datasets/issues/3822)

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, (.bin file)
- a corresponding rgb image (both 640x480 pixels),
- annotation ( present inside a .txt file)
 The ground truth is the 3D location of the head and its rotation.

The dataset structure is as follows : 
```
- 01.obj
- 01
     - frame_00003_depth.bin
     - frame_00003_pose.txt
     - frame_00003_rgb.png
     .
     .
     .
- 02.obj
 - 02
     - frame_00003_depth.bin
     - frame_00003_pose.txt
     - frame_00003_rgb.png
     .
     .
     .
```
Preview of frame_00003_pose.txt : 
```
0.988397 0.0731349 0.133128 
-0.0441539 0.976945 -0.208876 
-0.145334 0.200575 0.968838 

126.665 40.4515 876.198 
```
I have used the following dataset features : 
```
features=datasets.Features(
    {
        "person_id": datasets.Value("string"),
        "frame_number": datasets.Value("string"),
        "depth_image": datasets.Value("string"),
        "rgb_image": datasets.Image(),
        "3D_head_center": datasets.Array2D(shape=(3, 3), dtype="float"),
        "3D_head_rotation": datasets.Value("float"),
    }
```
I am giving the path to the depth_image here.

I need some inputs for the following : 

1. For each person, the dataset has the following additional information : 
```
For each sequence, the corresponding .obj file represents a head template deformed to match the neutral face of that specific person. [*.obj file]
In each folder, two .cal files contain calibration information for the depth and the color camera, e.g., the intrinsic camera matrix of the depth camera and the global rotation and translation to the rgb camera.
```
Wanted to know how we can represent these features ?

2. For _generate_examples , do I parse the directories and fetch the required information ? This would mean reading the .txt file to obtain the "3D_head_center" and "3D_head_rotation" details. We could precompute the features information  and have a metadata file and use the metadata file to yield information in _generate_examples ? Wanted your thoughts for the best approach for this ?

