Monday 19th of February 2018

Person Identification From Text Independent Lip Movement Using the Longest Matching Segment Method

Paul C. Brown

The use of lipreading as a standalone modality for biometric classification continues to gain ground but is still presented with several real world challenges. The paper presents a novel form of video temporal modelling using the Longest Matching Segment (LMS) method on a given baseline training model. LMS uses a Vector Quantization (VQ) model to encode full training video dynamics by mapping it to a frame sequence of maximum likelihood codewords. The model is applied to person identification from text independent lip movement on segmented test sets of the CMU-PIE, VidTIMIT and XM2VTS talking datasets and identification is based on the class with the longest matching segment. The results show that LMS improves the conventional VQ models especially when combined with dynamic delta features. Combined with magnitude 2D-FFT (Mag-2D-FFT) features, the system delivers comparable accuracies to full face recognition.

Keywords: Lip Movement, Vector Quantisation, Longest Matching Segment, Person Identification

Download Full-Text


Paul C. Brown
Paul C. Brown graduated from the University of the West Indies with a BSc. Hons Degree in Electrical and Computer Engineering in 1993 and a MSc in Digital Systems in 2001. He graduated from Queens University Belfast in 2017 with a PhD in person and word recognition from lip motion dynamics.

IJCSI Published Papers Indexed By:





IJCSI is a refereed open access international journal for scientific papers dealing in all areas of computer science research...

Learn more »
Join Us

Read the most frequently asked questions about IJCSI.

Frequently Asked Questions (FAQs) »
Get in touch

Phone: +230 911 5482

More contact details »