3D Face Tracking Using Stereo Camera

AlQahtani, Faleh; Chandran, Vinod; Banks, Jasmine

doi:10.1109/cais.2018.8441988

Cited by 6 publications

(2 citation statements)

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“…This complete system is divided into two parts; first to create the face database where each individual faces is captured and stored. Then, to test the video (Chrysos et al, 2015;Vasisht and Chen, 2017;Alqahtani et al, 2018) scene where people can be reliably detected, recognized, estimated and identified. We propose a new method to describe the facial feature by LBPH and CNN.…”

Section: Introductionmentioning

confidence: 99%

Automated Face Detection, Recognition and Gender Estimation Applied to Person Identification

Hannan¹,

Islam²,

Haque³

et al. 2019

Journal of Computer Science

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Face detection, recognition and gender estimation are one of the most significant research areas in computer vision, not only because of the challenging nature of faces as an object but also due to the countless applications that require the application of face detection, tracking and recognition. Although many significant types of research on face detection, recognition and gender estimation problems have done in the last few years separately, there is no particular research on face detection, recognition and gender estimation together from a real-time video for person identification. So, we feel that these types of significant research are still needed to work. The main contributions of our paper are divided into three parts, namely face detection, recognition and gender estimation for person identification. In our research work, we use Local Binary pattern Histogram (LBPH) method and Convolution Neural Network (CNN) to extract the facial features of face images whose computational complexity is very low. By calculating the Local Binary Patterns Histogram (LBPH) neighborhood pixels and Convolution levels, we extract effective facial feature to realize face recognition and gender estimation. We show the experimental results using these methods to recognize face and gender for person identification. CNN increase the calculating speed of testing real-time video and also improve the recognition rate. By using LPBH, we get 63% accuracy on average where CNN gives 99.88% training accuracy for face recognition-1 and 96.88% accurate for gender estimation-1 and 100% training accuracy for face recognition-2 and 93.38% training accuracy for gender estimation-2. However, Convolution Neural Networks (CNN) learns fast and predict efficiently.

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Section: Introductionmentioning

confidence: 99%

Automated Face Detection, Recognition and Gender Estimation Applied to Person Identification

Hannan¹,

Islam²,

Haque³

et al. 2019

Journal of Computer Science

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“…With the increased use of video surveillance in institutions, such as schools, hospitals, shopping malls, banks, offices, and factories, video analysis techniques and algorithms have been developed to find mechanisms by which faces can be identified with ease, regardless of the person on camera. In light of these advancements, this paper built on the work of Alqahtani et al [1], whose methods are being developed for Manuscript received February 7, 2018; revised June 6, 2018. The authors are with Queensland University of Technology, Australia (e-mail:…”

Section: Introductionmentioning

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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