FB pixel

Scientists say they’ve improved on emotion recognition from a person’s gait

Categories Biometric R&D  |  Biometrics News
Scientists say they’ve improved on emotion recognition from a person’s gait

Researchers continue to try to refine methods of reading a person’s mood from their gait biometrics, and the math gets deep fast.

Enough people around the world are spooked by efforts to identify people based on their gait and to read the emotions of people from real-time videos of their faces. Getting emotional reads on person based on how they walk (presumably along with their identity) is a lesser-known area of research that likely will increase the unease with biometric surveillance.

There are many papers, peer-reviewed and not, prodding this possibility to life, so methods are competing with one another.

This fall, a team of ostensibly academic Chinese researchers published a non-peer reviewed paper that begins with the observation that “the human skeleton is naturally a graph in non-Euclidean space.”

Ostensible because of the well-documented, prevalent military involvement in biometric surveillance. Beijing has deployed a public facial recognition system second to none against its own people, including nationwide campaigns to jail religious and ethnic minorities.

This team included members from the School of Computer Science and Technology, Chongqing University and Nanning Normal University. One member was with the Australian Artificial Intelligence Institute at the University of Technology Sydney.

Some of the most popular methods used in this enterprise, the author writes, are too inflexible or suffer from insufficient emotional information to perform their assigned tasks well enough.

The researchers’ solution was what they call a multi-scale adaptive graph convolution network (also known by the equally obscuring MSA-GCN).

They claim to be able to get “more discriminative and robust emotional features to recognize emotion.”

The algorithm is using coarse-grained graphs to extract overall data of a gait and fine-grained graphs to uncover local information. This approach solves the inflexible/overwhelmed shortcomings of prominent gait recognition efforts, according to the researchers.

MSA-GCN, used on two public datasets, reportedly improved mAP by 2 percent compared to other state-of-the-art algorithms. MAP is object detection accuracy and is applied to biometrics, motion recognition, image classification and other tasks.

Article Topics

 |   |   | 

Latest Biometrics News


Biometric ID cards remain foundational, but don’t count out fingerprint smart cards

Biometric national ID cards top the most-read news of the week on Biometric Update, between a contract in Cameroon for…


DHS and TSA adjust digital strategies with biometrics, facial recognition

U.S. government agencies are adapting in real time to a digital landscape transformed by AI, identity fraud, deepfakes and biometric…


Finger vein biometrics from Global ID deployed in Namibia’s fight against HIV

A new application of Global ID’s finger vein biometrics to help reduce HIV Infections among vulnerable young people is launching…


Bill allowing biometric age verification for booze sales moves to Missouri Senate

The Maryville Forum reports that Missouri retailers could soon perform age verification using biometric methods such as facial recognition or…


Victorians sign up for mobile driving licenses in droves but let down at the pub

Victoria reports 200,000 people signing up for mobile driving licenses (mDLs) within the first 48 hours of their introduction this…


Retail biometrics queues up from shopping malls to quick serve restaurants

Despite skepticism among American consumers, biometric payments in retail are about to have a moment, according to some experts. These…


Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Most Read This Week

Featured Company

Biometrics Insight, Opinion

Digital ID In-Depth

Biometrics White Papers

Biometrics Events