An improved neural-network-based face detection and facial expression classification system

Stathopoulou, Ioanna-Ourania; Tsihrintzis, George A.

doi:10.1109/icsmc.2004.1398376

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…Deep learning can be used in Intelligent Tutoring Systems at many levels and for varying functionalities. For example, in [155] neural networks have been employed…”

Section: Ai Techniques For the Ux Of Intelligent Tutoring Systemsmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

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“…Deep learning can be used in Intelligent Tutoring Systems at many levels and for varying functionalities. For example, in [155] neural networks have been employed…”

Section: Ai Techniques For the Ux Of Intelligent Tutoring Systemsmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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“…Reported analyses of predicting human emotions from biometrics have mostly been investigated with respect to the face modality [5–7], but also for the voice [8], gait [9] and keystroke [10] modalities.…”

Section: Introductionmentioning

confidence: 99%

Study of automatic prediction of emotion from handwriting samples

Fairhurst¹,

Erbilek²,

Li³

2015

IET biom.

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Handwriting biometrics has a long history, especially when the handwritten signature is the target, but it has also proved possible to use handwriting as a basis for the prediction of various non-unique but forensically useful characteristics of the writer, generally considered to be examples of so-called 'soft biometrics'. Most commonly, these are characteristics such as the age or gender of the writer, but the predictive capabilities arising in handwriting offer wider opportunities for trait prediction. This study presents a preliminary investigation of the use of handwriting to predict information about the writer relating specifically to higher level characteristics such as emotional state. The authors present an initial study to demonstrate that this is possible, and explore a number of factors particularly relevant to the use of such a capability in areas of forensic investigation.

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“…However, appearance based methods have inherent high computational load, and the facial image size needs to be fixed. On the other hand, feature based methods have been developed to extract facial feature points for expression recognition [6,7]. The distances between feature points are regarded as feature values and these data are used to classify different facial expressions.…”

Section: Introductionmentioning

confidence: 99%

A Robotic Facial Expression Recognition System Using Real-Time Vision System

Song

Han

Chang

et al. 2008

KEM

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The capability of recognizing human facial expression plays an important role in advanced human-robot interaction development. Through recognizing facial expressions, a robot can interact with a user in a more natural and friendly manner. In this paper, we proposed a facial expression recognition system based on an embedded image processing platform to classify different facial expressions on-line in real time. A low-cost embedded vision system has been designed and realized for robotic applications using a CMOS image sensor and digital signal processor (DSP). The current design acquires thirty 640x480 image frames per second (30 fps). The proposed emotion recognition algorithm has been successfully implemented on the real-time vision system. Experimental results on a pet robot show that the robot can interact with a person in a responding manner. The developed image processing platform is effective for accelerating the recognition speed to 25 recognitions per second with an average on-line recognition rate of 74.4% for five facial expressions.

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An improved neural-network-based face detection and facial expression classification system

Cited by 15 publications

References 15 publications

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Study of automatic prediction of emotion from handwriting samples

A Robotic Facial Expression Recognition System Using Real-Time Vision System

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