A face tracking method using feature point tracking

Lin, Guo-Shiang; Tsai, Tung-Sheng

doi:10.1109/isic.2012.6449743

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…Researchers are pushing forward the frontiers for each step. For the first step, Lin and Tsai (2012) and Ranganatha and Gowramma (2017) both have focused on face detection and face tracking problems. They have use Haarlike features such as face edges and corners to find all the faces in a frame and apply different tracking algorithms for the faces in the coming frames to increase computation efficiency.…”

Section: Gaze Detectionmentioning

confidence: 99%

You Talkin’ to Me? A Practical Attention-Aware Embodied Agent

Divekar

Kephart

Mou

et al. 2019

Human-Computer Interaction – INTERACT 2019

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Most present-day voice-based assistants require that users utter a wake-up word to signify that they are addressing the assistant. While this may be acceptable for one-shot requests such as "Turn on the lights", it becomes tiresome when one is engaged in an extended interaction with such an assistant. To support the goal of developing low-complexity, low-cost alternatives to a wake-up word, we present the results of two studies in which users engage with an assistant that infers whether it is being addressed from the user's head orientation. In the first experiment, we collected informal user feedback regarding a relatively simple application of head orientation as a substitute for a wake-up word. We discuss that feedback and how it influenced the design of a second prototype assistant designed to correct many of the issues identified in the first experiment. The most promising insight was that users were willing to adapt to the interface, leading us to hypothesize that it would be beneficial to provide visual feedback about the assistant's belief about the user's attentional state. In a second experiment conducted using the improved assistant, we collected more formal user feedback on likability and usability and used it to establish that, with high confidence, head orientation combined with visual feedback is preferable to the traditional wake-up word approach. We describe the visual feedback mechanisms and quantify their usefulness in the second experiment.

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Section: Gaze Detectionmentioning

confidence: 99%

You Talkin’ to Me? A Practical Attention-Aware Embodied Agent

Divekar

Kephart

Mou

et al. 2019

Human-Computer Interaction – INTERACT 2019

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“…Guo-Shiang Lin and Tung-Sheng Tsai have found that to detect facial regions, a face detector [7] based on Haar-like features trained by Adaboost algorithm is adopted.…”

Section: Face Tracking Processmentioning

confidence: 99%

“…Method 1: Square difference matching method matches the squared difference (7). Therefore, a perfect match will give a value of zero and a bad match will give a large value.…”

Section: ) Simple Template Matchingmentioning

confidence: 99%

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Face tracking: State of the art

Rabhi

Sadiq

Mouloudi

2015

2015 Third World Conference on Complex Systems (WCCS)

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Face detection and tracking is challenging problem in image processing and computer vision. In the last few years, it has received a great attention because of its many applications based on various methods in different fields such as law enforcement, security and so on. Face detection and tracking are the two processes done by using various approaches. It is applied mainly in surveillance. The main purpose of these processes are detect and track the face even in poor viewing conditions in surveillance application. This paper describes various techniques used for face tracking, their advantages and their disadvantages, and we discuss the comparison between these techniques.

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“…The face tracking is possible by extracting the face features [2]. There were different methods available for tracking [3].…”

Section: Introductionmentioning

confidence: 99%

An Optimised Allotment and Tracking Using Django and Opencv

Kathi¹,

Shaik²

2021

ISI

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Developments in tracking of objects is one of the key breakthroughs in computer vision in recent years. A video is a continuous flow of frames. By analysing the difference between a frame to its successive, one can estimate the movement of object. In this paper, the movement of person is detected with the help of two cameras facing opposite to each other. The detected persons face is recognized with the faces in the database, his data about the movement is updated in the excel sheet from time to time and the same is applied in the real-world problem of vigilance on invigilators in the examination hall. Examinations are inescapable to conclude a course. Devising the students and watchdogs into the examination halls are the part of proper conduction of Examinations. One of the key things in the administration of examinations is the issuance of invigilations. The faculty act as invigilators. It is arduous to mull over his/her designation and experience while earmarking the invigilations manually. This paper presents a cybernetic and contemplative method of dole out using Django and tracking the movements of each invigilator using Opencv.

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A face tracking method using feature point tracking

Cited by 9 publications

References 3 publications

You Talkin’ to Me? A Practical Attention-Aware Embodied Agent

You Talkin’ to Me? A Practical Attention-Aware Embodied Agent

Face tracking: State of the art

An Optimised Allotment and Tracking Using Django and Opencv

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