Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 32, Pages: 1-7
Aparna Brahme1* and Umesh Bhadade2
1 Department of Information Technology Engineering, MET’s Institute of Engineering, Nashik - 422207, Maharashtra, India; [email protected]
2 Department of IT Engineering, SSBT’s College of Engineering and Technology Bhambori, Jalgaon – 425001, Maharashtra, India; [email protected]
*Author for correspondence
Department of Information Technology Engineering
Background/Objectives: The aim of our research is to guess the language of spoken utterance by using the cues from visual speech recognition i.e. from movement of lips. The first step towards this task is to detect lips form face image and then to extract various geometric features of lip shape in order to guess the utterance. Methods/Statistical Analysis: This paper presents the methodology for detecting lips from face images using constrained local model (CLM) and then extracting the geometric features of lip shape. The two steps involved in lip detection are CLM model building and CLM search. For extracting lip geometric features, twenty feature points are defined on lips and lip height, width, area are defined using these twenty feature points. Findings: CLM model is build using images from FGnet Talking face video database and tested using images from FGnet Talking face video database and also using other images. The detection accuracy is more for FGnet images as compare to other images. Feature vector defining the lip shape consists of geometric parameters like height, width and area of inner and outer lip contours. Feature vector is calculated for all test images after detecting lips from face image. So the error in detecting lips leads to the error in feature vector. This indicates the speaker dependency of visual speech recognition systems. Application/Improvements: The proposed approach is useful in visual speech recognition for lip detection and feature extraction. Minimizing the speaker dependency and generalizing the approach should be considered for further improvements.
Keywords: CLM, Lip Detection, Language Identification, Visual Speech
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