Real-time detection of nodding and head-shaking by directly detecting and tracking the "between-eyes"

Kawato, Shinjiro; Ohya, Jun

doi:10.1109/afgr.2000.840610

Cited by 86 publications

(58 citation statements)

References 7 publications

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“…The acquisition of these measurements depends on the tools that are currently being developed towards automatic analysis of facial behaviour (Bartlett et al 1999;Cohn et al 1999; for a review see Pantic and Rothkrantz 2000) and the extraction of vocal parameters (Boersma 2001). Automatic analysis of postures and gestures still represents a challenge for SSP but progresses have been made on the detection of head movements (Kapoor and Picard 2001;Kawato and Ohya 2000;Tan and Rong 2003) as well as hand gestures (Erol et al 2007;Morency et al 2005) and body movements (Oikonomopoulos et al 2009). …”

Section: Multimodal Signalsmentioning

confidence: 99%

A psycho-ethological approach to social signal processing

Méhu

Scherer

2012

Cogn Process

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The emerging field of social signal processing can benefit from a theoretical framework to guide future research activities. The present article aims at drawing attention to two areas of research that devoted considerable efforts to the understanding of social behaviour: ethology and social psychology. With a long tradition in the study of animal signals, ethology and evolutionary biology have developed theoretical concepts to account for the functional significance of signalling. For example, the consideration of divergent selective pressures responsible for the evolution of signalling and social cognition emphasized the importance of two classes of indicators: informative cues and communicative signals. Social psychology, on the other hand, investigates emotional expression and interpersonal relationships, with a focus on the mechanisms underlying the production and interpretation of social signals and cues. Based on the theoretical considerations developed in these two fields, we propose a model that integrates the processing of perceivable individual features (social signals and cues) with contextual information, and we suggest that output of computer-based processing systems should be derived in terms of functional significance rather than in terms of absolute conceptual meaning.

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Section: Multimodal Signalsmentioning

confidence: 99%

A psycho-ethological approach to social signal processing

Méhu

Scherer

2012

Cogn Process

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“…Similarly, the authors of [2] present a system that uses a customized IR camera for pupil tracking and a discrete Hidden Markov Model to detect head nod and shake. Kawato and Ohya [3] have described another system to detect head nods and shakes in real time by directly detecting and tracking the between-eyes region using a webcam. Combining the circle frequency filter together with skin color information and template, the between-eyes region could be detected and tracked.…”

Section: Releated Workmentioning

confidence: 99%

Real-time head nod and shake detection for continuous human affect recognition

Wei

Scanlon²,

et al. 2013

2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)

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Human affect recognition is the field of study associated with using automatic techniques to identify human emotion or human affective state. A person's affective states is often communicated non-verbally through body language. A large part of human body language communication is the use of head gestures. Almost all cultures use subtle head movements to convey meaning. Two of the most common and distinct head gestures are the head nod and the head shake gestures. In this paper we present a robust system to automatically detect head nod and shakes. We employ the Microsoft Kinect and utilise discrete Hidden Markov Models (HMMs) as the backbone to a to a machine learning based classifier within the system. The system achieves 86% accuracy on test datasets and results are provided.

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“…Inspired by [10], we apply "between eyes" template matching on our system. "Between eyes" is the region between two eyes and has the property of invariant to both open and closed eyes circumstances.…”

Section: Algorithm Implementation Face and Eye Detectionmentioning

confidence: 99%

Detecting Driver Drowsiness Using Eye State

Liu¹,

Tan²

2018

dtcse

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Various studies have suggested that 20%-50% road accidents are fatigue-related. Nevertheless, on certain roads, the maximum value is out of scope. This paper aims at developing a vision-based drowsiness detecting system which is noninvasive and accurate to help alert drowsy driver in advance. Eye state is used to evaluate drowsiness. First, face region is detected through AdaBoost with Haar feature. Then instead of applying old fashioned eye location methods which many drowsiness detect systems still possess, our system obtain eye region through a customized state of art face alignment algorithm based on Regressing Local Feature. Finally, an SVM is trained to distinguish eye states. Results demonstrate that our system can achieve a high detection rate in various lighting circumstances. INTRODUCTION

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Real-time detection of nodding and head-shaking by directly detecting and tracking the "between-eyes"

Cited by 86 publications

References 7 publications

A psycho-ethological approach to social signal processing

A psycho-ethological approach to social signal processing

Real-time head nod and shake detection for continuous human affect recognition

Detecting Driver Drowsiness Using Eye State

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