Automatic lip contour extraction from color images

WANG, S; Lau, Wai Kin; Leung, S.W.

doi:10.1016/s0031-3203(04)00196-7

Cited by 38 publications

(13 citation statements)

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“…of localized color active contour model ( LCACM) expand from scheme ,introduced by [19] given that the foreground and background regions with variation in color space. They utilize a 16-point deformable model by [20]with geometric constraints to achieve lip contour extraction . They used deform modelled location region base to approach lip tracking combined with the extraction of lips contour in the color image [21].…”

Section: Lips Transformationmentioning

confidence: 99%

4D Automatic Lip-Reading for Speaker's Face Identification

AbUShana¹

2018

SIPIJ

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Section: Lips Transformationmentioning

confidence: 99%

4D Automatic Lip-Reading for Speaker's Face Identification

AbUShana¹

2018

SIPIJ

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“…At the middle level, the mouth is classified into a few discrete states: open or closed [163,164], lip corner pulled up or down [116], smile or not [165,166]. At the finest level, the mouth is accurately tracked and characterized for visual speech recognition [167][168][169].…”

Section: Detecting Mouth Motionmentioning

confidence: 99%

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Lin

Levine

2016

Computer Vision and Image Understanding

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During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that may result. Studies have shown that most untrained people cannot perform well in discerning liars and truth-tellers.Thus it has become common to adopt various technical aids to compensate for this poor judgment. Examples are polygraphs, functional Magnetic Resonance Imaging (fMRI) and linguistic analysis. However, the deception indicators used in these cases are not reliable.In the popular TV program Lie to Me, micro-expressions have been used for detecting deceit during the investigation of some criminal cases. A microexpression is considered to be a rapid and involuntary facial expression which could reveal the concealed emotion. Additionally, some psychological studies have stated that certain facial actions are more difficult to inhibit if the associated facial expressions are genuine. Similarly, these facial expressions are equally difficult to fake. This has cast light on the possibility that deception could be detected by analyzing these facial actions. However, to the best knowledge of the author, there is no computer vision research that has attempted to discriminate high-stakes deception from truth using facial expressions. Therefore, this thesis aims to test the validity of facial clues to deception detection in high-stakes situations using computer vision approaches.We note that only a limited number of the existing databases have been collected specifically for deception detection studies and none of them were obtained from real-world situations. In this thesis we present a video database of actual highstakes situations, which we have created using YouTube.We have adopted 2D appearance-based methods as the methodology to characterize the 3D facial features. Instead of building a 3D head model as is the current trend, we have extracted invariant 2D features that are related to the 3D characteristic.iii In order to discern deception and honesty, we have identified the following deceptive cues from nine separate facial regions through dynamic facial analysis: eye blink, eyebrow motion, wrinkle occurrence and mouth motion. Then these cues were integrated to form a facial behavior pattern vector. A Random Forest was trained using the collected database and applied to classify the facial patterns into deceptive and truthful categories.Despite the many uncontrolled factors (illumination, head pose and facial occlusion) in the videos in our database, we have achieved an accuracy of 76.92% when discriminating liars from truth-tellers using both micro-expressions and

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“…2, we initially crop the mouth regions of interest (ROI) from the recorded lip sequences and extract the lip contours [32] frame by frame. Then, we employ a 16-point lip model proposed by Wang et al [33] to compute nine geometric shape parameters, i.e. maximum horizontal distance, seven vertical distances and mouth area, denoted as…”

Section: A Visual Feature Extractionmentioning

confidence: 99%

Learning Multi-Boosted HMMs for Lip-Password Based Speaker Verification

Liu

Cheung

2014

IEEE Trans.Inform.Forensic Secur.

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Abstract-This paper proposes a concept of lip motion password (simply called lip-password hereinafter), which is composed of a password embedded in the lip movement and the underlying characteristic of lip motion. It provides a double security to a visual speaker verification system, where the speaker is verified by both of the private password information and the underlying behavioral biometrics of lip motions simultaneously. Accordingly, the target speaker saying the wrong password or an impostor who knows the correct password will be detected and rejected. To this end, we shall present a multi-boosted Hidden Markov model (HMM) learning approach to such a system. Initially, we extract a group of representative visual features to characterize each lip frame. Then, an effective lip motion segmentation algorithm is addressed to segment the lip-password sequence into a small set of distinguishable subunits. Subsequently, we integrate HMMs with boosting learning framework associated with a random subspace method and data sharing scheme to formulate a precise decision boundary for these subunits verification, featuring on high discrimination power. Finally, the lip-password, whether spoken by the target speaker with the pre-registered password or not, is identified based on all the subunit verification results learned from multi-boosted HMMs. The experimental results show that the proposed approach performs favorably compared with the state-of-the-art methods.Index Terms-Lip-password, lip motion segmentation, multi-boosted HMMs, random subspace method, data sharing scheme.

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Automatic lip contour extraction from color images

Cited by 38 publications

References 0 publications

4D Automatic Lip-Reading for Speaker's Face Identification

4D Automatic Lip-Reading for Speaker's Face Identification

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Learning Multi-Boosted HMMs for Lip-Password Based Speaker Verification

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