An annotated image from IBM's Diversity in Faces dataset for facial recognition systems.

IBM thinks the data being used to train facial recognition systems isn't diverse enough.

The tech giant released a trove of data containing 1 million images of faces taken from a Flickr dataset with 100 million photos and videos.

The images are annotated with tags related to features including craniofacial measurements, facial symmetry, age and gender.

Researchers at the company hope that these specific details will help developers train their artificial intelligence-powered facial recognition systems to identify faces more fairly and accurately.

"Facial recognition technology should be fair and accurate," John Smith, a fellow and lead scientist at IBM, told CNBC by email. "In order for the technology to advance it needs to be built on diverse training data."

Smith stressed the importance of variety in datasets for facial recognition systems to reflect real-world diversity and reduce the rate of error in matching a face to a person.

"Many prominent datasets used in the field are too narrow and fall short in coverage and balance," he said. "The data does not reflect the faces we see in the world."