Top Biometric Video Datasets of 2022

Finding high-quality biometric video datasets can be tricky, especially if you need particular demographics, lighting conditions, or video durations. 

That’s why at Twine we specialize in helping companies create custom biometric video datasets and will help you get the data you need, no matter the demographics or requirements. 

If you’re specifically looking for an off-the-shelf dataset then we’ve done the hard work for you. Without further ado, here are the best biometric video datasets for you.

So, are you ready to dive in?

Let’s get into our list of the Best Biometric Video Datasets in 2022.

Here are our top picks for the Best Biometric Video Datasets out there:

1. The Largest Biometric Video Dataset

The ND-Collection J2 Dataset is meant to aid research efforts in the general area of developing, testing, and evaluating human recognition algorithms. 1800 3D (and corresponding 2D) profile (ear) images from 415 human subjects captured between 2003 and 2005.  Corresponds to data used in Yan and Bowyer, “Biometric recognition using three-dimensional ear shape, “PAMI 29(8), August 2007.

Access the dataset


2. Best Soft Biometric Video Dataset

The first release of the BIOMDATA dataset collection contains image and sound files for six biometric modalities:

  • Iris
  • Face
  • Voice
  • Fingerprint
  • Hand Geometry
  • Palm Print

The dataset also includes soft biometrics such as height and weight, for subjects of different age groups, ethnicity, and gender with variable numbers of sessions/subject.

Access the dataset


  • The SoBIS (Soft Biometric in Surveillance Dataset) was recorded by seven cameras over three days. 31 volunteers reappear multiple times, with scenes ranging from low complexity with only one person in the camera network to high complexity with 15+ persons in the recorded area.

3. Best Biometric Multi-Modal Recognition Systems Dataset

This Data Science Journal dataset consists of videos related to 67 different subjects. The videos contain similar text and the text contains digits from 1 to 20 recited by 67 different subjects using the same experimental setup. This dataset can be used as a unique resource for researchers and analysts for training deep neural networks to build highly efficient and accurate recognition models in various domains of computer vision such as face recognition model, expression recognition model, speech recognition model, text recognition, etc.

Access the dataset


4. Best People Identification Biometic Video Dataset

This Gotcha-I dataset has been obtained using more mobile cameras to adhere to the data of BWCs. It includes videos from 62 subjects in indoor and outdoor environments, addressing both security and surveillance problems. This dataset is composed of 493 videos – including a set of 180 videos for each face of the subjects in the dataset. Furthermore, there are already processed data, such as the 3D model of the face of each subject with all the poses of the head in pitch, yaw, and roll; and the body key point coordinates of the gait for each video frame.

Access the dataset


  • The CelebA Dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. 

Wrapping up

To conclude, here are top picks for the best Biometric Video datasets:

We hope that this list has either helped you find a dataset for your project or, realize the myriad of options available to you. 

If there are any datasets you would like us to add to the list then please let us know here.

If you would like to find out more about how we could help build a custom dataset for your project then please don’t hesitate to contact us!

Let us help you do the math – check our AI dataset project calculator.

Ready to learn more? Check out our Dataset Archives:

Twine AI

Harness Twine’s established global community of over 400,000 freelancers from 190+ countries to scale your dataset collection quickly. We have systems to record, annotate and verify custom video datasets at an order of magnitude lower cost than existing methods.