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Consistent and Accurate Face Tracking and Recognition in Videos

Liu, Yiran
http://orcid.org/0000-0002-9928-5303
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1588598739996101

Abstract Details

2020, Master of Science (MS), Ohio University, Computer Science (Engineering and Technology).
Automatically tracking and recognizing human faces in videos and live streams is often a crucial component in many high-level applications such as security, visual surveillance and human-computer interaction. Deep learning has recently revolutionized artificial intelligence areas, including face recognition and detection. Most of the existing video analysis solutions, however, rely on certain 2D convolutional neural network (CNN) to process video clips upon a frame-to-frame basis. The temporal contextual information between consecutive frames is often inadvertently overlooked, resulting in inconsistent tracking outcomes, which also negatively affect the accuracy of human identification. To provide a remedy, we propose a novel network framework that allows history information be carried along video frames. More specifically, we take the single short scale-invariant face detection (S3FD) as the baseline face detection network and combine it with long short-term memory (LSTM) components to integrate temporal context. Taking the images and detection results of previous frames as additional inputs, our S3FD + LSTM framework is well posed to produce more consistent and smoother face detection results along time, which in return leads to more robust and accurate face recognition in videos and live streams. We evaluated our face tracking and recognition model with both public (YouTube Face) and self-made datasets. Experimental results demonstrate that our S3FD+LSTM approach constantly produces smoother and more stable bounding boxes than S3FD alone. Recognition accuracy is also improved over the baseline model, and our model significantly outperforms the state-of-the-art face tracking solutions in the public domain.
Jundong Liu (Advisor)
70 p.
Computer Science
Face recognition; Face tracking; Deep learning; Consistent; LSTM

Recommended Citations

Citations

  • Liu, Y. (2020). Consistent and Accurate Face Tracking and Recognition in Videos [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1588598739996101

    APA Style (7th edition)

  • Liu, Yiran. Consistent and Accurate Face Tracking and Recognition in Videos. 2020. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1588598739996101.

    MLA Style (8th edition)

  • Liu, Yiran. "Consistent and Accurate Face Tracking and Recognition in Videos." Master's thesis, Ohio University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1588598739996101

    Chicago Manual of Style (17th edition)

ohiou1588598739996101
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© 2020, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.