Automatic visual speech segmentation and recognition using directional motion history images and Zernike moments

Shaikh, Ayaz A.; Kumar, Deepak; Gubbi, Jayavardhana

doi:10.1007/s00371-012-0751-7

Cited by 7 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be found that the result obtained by the proposed approach is close to the ground truth and the segmentation errors are small. Apparently, the proposed approach outperforms the method [38] and is even promisingly comparable to the Method [37] and [39]. Importantly, the proposed approach just utilizes the extracted mouth area and does not compute the intensity change of every pixel frame by frame.…”

Section: ]mentioning

confidence: 99%

“…Talea et al [38] first obtained the mouth areas of the consecutive frames and then made a series of mouth area subtractions associated with a smoothing filtering for syllable separation. Recently, Shaikh et al [39] have utilized an ad hoc method for temporal viseme segmentation (i.e. 14 different mouth activities) based on the pair-wise pixel comparison of consecutive images.…”

Section: B Lip Motion Segmentationmentioning

confidence: 99%

“…We have carefully adjusted the parameters of methods [37]- [39] and selected the best results from three runs to make a comparison. Various digital lip-password sequences have been tested and a typical example is shown in Fig.…”

Section: ]mentioning

confidence: 99%

See 2 more Smart Citations

Learning Multi-Boosted HMMs for Lip-Password Based Speaker Verification

Liu

Cheung

2014

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

Abstract-This paper proposes a concept of lip motion password (simply called lip-password hereinafter), which is composed of a password embedded in the lip movement and the underlying characteristic of lip motion. It provides a double security to a visual speaker verification system, where the speaker is verified by both of the private password information and the underlying behavioral biometrics of lip motions simultaneously. Accordingly, the target speaker saying the wrong password or an impostor who knows the correct password will be detected and rejected. To this end, we shall present a multi-boosted Hidden Markov model (HMM) learning approach to such a system. Initially, we extract a group of representative visual features to characterize each lip frame. Then, an effective lip motion segmentation algorithm is addressed to segment the lip-password sequence into a small set of distinguishable subunits. Subsequently, we integrate HMMs with boosting learning framework associated with a random subspace method and data sharing scheme to formulate a precise decision boundary for these subunits verification, featuring on high discrimination power. Finally, the lip-password, whether spoken by the target speaker with the pre-registered password or not, is identified based on all the subunit verification results learned from multi-boosted HMMs. The experimental results show that the proposed approach performs favorably compared with the state-of-the-art methods.Index Terms-Lip-password, lip motion segmentation, multi-boosted HMMs, random subspace method, data sharing scheme.

show abstract

Section: ]mentioning

confidence: 99%

Section: B Lip Motion Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Learning Multi-Boosted HMMs for Lip-Password Based Speaker Verification

Liu

Cheung

2014

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

“…The result is a scalar-valued image compared with which moving pixels are brighter [15]. Examples of MHI are presented in Figure 7(b).…”

Section: Detecting and Tracking Pedestrianmentioning

confidence: 99%

Pedestrian Detection and Tracking for Counting Applications in Metro Station

Chen

Zhou

et al. 2014

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

A pedestrian counting method based on Haar-like detection and template-matching algorithm is presented. The aim of the method is to count pedestrians that are in a metro station automatically using video surveillance camera. The most challenging problem is to count pedestrians accurately in the case of not changing the position of the surveillance camera, because the view that surveillance camera uses in a metro station is always short-shot and nondirect downward view. In this view, traditional methods find it difficult to count pedestrians accurately. Hence, we propose this novel method. In addition, in order to improve counting accuracy more, we present a method to set the parameter value with a threshold-curve instead of a fixed threshold. The results of experiments show the high accuracy of our method.

show abstract