Real-time face tracking with instability using a feature-based adaptive model

Nhat, Vo Quang; Kim, Soo-Hyung; Yang, Hyung Jeong; Lee, Guee-Sang

doi:10.1007/s12555-014-0126-0

Cited by 5 publications

(1 citation statement)

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“…3D facial recognition has been developed to enhance facial identification for individuals by using specific features that are unique to those individuals [2]- [9]. Face tracking relies on the extraction of random uniformly distributed points on the face of a human being, which are first generated in a 2D form and later used to generate a 3Dface model based on head movements, changes in pose, and the introduction of new feature points [10]- [13].The process of tracking faces with the goal of improving human-computer interactions is prone to errors due to the extreme number of computations required to capture real-time information from an individual.…”

Section: Related Workmentioning

confidence: 99%

Detection and Tracking of Faces in 3D Using a Stereo Camera Arrangements

AlQahtani¹,

Banks²,

Chandran³

et al. 2019

IJMLC

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3D facial tracking has become vital to the continued integration of computers, technology, and human society. In recent decades, the integration of technology has increased, and the use of surveillance, conference calls, gaming components, and other similar applications has spurred demand for the ability to recognize the distinctive features of humans. However, in order for these new technologies to function effectively and reach their fullest potential, a great deal of work is still needed. The field of facial mapping and tracking is still in its early developmental stages, necessitating additional research into the best methods of tracking and monitoring specific human faces. To this end, an algorithm has been created that would allow for improvements in this area; however, a video was first required that could be used effectively for the algorithm. Two web cameras running on Raspberry Pi were used to gather the footage necessary for detecting and tracking specific facial features. While certain limitations were identified throughout the process, the algorithm still achieved significant successful tracking results. In spite of this success, further efforts are still needed to effectively explore the proposed algorithm and improve upon these initial results.

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