Statistical models of appearance for eye tracking and eye-blink detection and measurement

Bacivarov, Ioana; Ionita, Mircea C.; Corcoran, Peter

doi:10.1109/tce.2008.4637622

Cited by 98 publications

(48 citation statements)

References 22 publications

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“…[123,124,130], measuring the vertical motion using optical flow [128,[131][132][133], computing the eye openness by miscellaneous methods [125,126,[134][135][136][137], classifying the eye state using statistical approaches [138][139][140][141], and other methods [127,129,142].…”

Section: Eye Blink Detectionmentioning

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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Section: Eye Blink Detectionmentioning

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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“…Bacivarov et al [21] proposed a straightforward proof-ofconcept model, having as an advantage that it models properly the eye region for both eyes open and closed by using A statistical Active Appearance (AAM) based techniques. It can be adapted to work in real-time.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Real Time Eye-Blink Detection System

Galab¹,

Abdalkader²,

Zayed³

2014

IJCA

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The eye is one of the sense organs that can give users better interaction closer to their need by observing the change of the eyes (open or closed). It is considered as a rich source for gathering information on our daily life. So, it is used in computer science area, especially in human computer interaction. This paper proposes a new system for detecting eye blinks accurately without any restriction on the background and the user does not have to wear any sensors or marks. No manual initialization is required in our proposed system. The proposed system works with the online and offline environment. It automatically classifies the eye as either open or closed at each video frame. The proposed system is tested with the users who wear glasses and the experiments proved its applicability. The proposed system is very easy to configure and use. It is totally non-intrusive and it only requires one low-cost web camera and computer.

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“…We conduct a study in order to verify the amount of information individually brought by facial features in an automatic expression recognition system. A thorough description of the eye area is done using an AAM model, as described in our previous work [16]. A detailed AAM lip model, including a hue filtering is proposed by us for the lips area in [17].…”

Section: A Relevant Facial Features To Indicate Expressivitymentioning

confidence: 99%