Algorithm for multiple faces tracking

Lerdsudwichai, Charay; Abdel-Mottaleb, Mohamed

doi:10.1109/icme.2003.1221732

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…There is a lot of literature on methods of feature extraction methods [111,9,65,149,119,90] and tracking faces through images, [33,146,102,83,36] for lip-reading. However, to date, there is no one accepted method as the de facto method for extracting lip-reading features.…”

Section: The Research Problemmentioning

confidence: 99%

Decoding visemes: Improving machine lip-reading

Bear

Harvey

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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To undertake machine lip-reading, we try to recognise speech from a visual signal. Current work often uses viseme classification supported by language models with varying degrees of success. A few recent works suggest phoneme classification, in the right circumstances, can outperform viseme classification. In this work we present a novel two-pass method of training phoneme classifiers which uses previously trained visemes in the first pass. With our new training algorithm, we show classification performance which significantly improves on previous lip-reading results.

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Section: The Research Problemmentioning

confidence: 99%

Decoding visemes: Improving machine lip-reading

Bear

Harvey

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Others [1] [3], have detected frontal faces using cascades of simple classifiers. Some strategies for tracking the resulting face regions include techniques based on the overlapping of bounding boxes [4] [9], techniques using partial Kalman filtering for motion prediction [4], and techniques which use the mean shift method for tracking [5]. While the topics discussed by this paper do not assume the use of any one particular face detection or tracking strategy, our results were obtained using cascading Haar classifiers (as implemented in the Intel R OpenCV library) to detect frontal faces.…”

Section: Previous Researchmentioning

confidence: 99%

“…Regarding the detection of faces for the purpose of tracking, many researchers [4] [9] [5] have suggested techniques that involve the use of skin color segmentation to locate candidate face regions. Others [1] [3], have detected frontal faces using cascades of simple classifiers.…”

Section: Previous Researchmentioning

confidence: 99%

Constructing Face Image Logs that are Both Complete and Concise

Fourney

Laganière

2007

Fourth Canadian Conference on Computer and Robot Vision (CRV '07)

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This paper describes a construct that we call a face image log. Face image logs are collections of time stamped images representing faces detected in surveillance videos. The techniques demonstrated in this paper strive to construct face image logs that are complete and concise in the sense that the logs contain only the best images available for each individual observed. We begin by describing how to assess and compare the quality of face images. We then illustrate a robust method for selecting high quality images. This selection process takes into consideration the limitations inherent in existing face detection and person tracking techniques. Experimental results demonstrate that face logs constructed in this manner generally contain fewer than 5% of all detected faces, yet these faces are of high quality, and they represent all individuals detected in the video sequence.

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“…Real-Time object tracking has many applications such as surveillance, perceptual user interfaces, augmented reality, smart rooms, object-based video compression and driver assistance [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%