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Automatic visual lipreading, an efficient and convenient way of human–machine interaction, recognizes the content of the conversation from dynamic visual features of the speakers. Automatic lipreading based on acoustic speech alone can effectively prevent interference in complex environment, particularly under noisy conditions. In this paper, we propose novel visual extraction and HMM classification methods for automatic lipreading system, which reduce the dimension by using locally sensitive discriminant analysis algorithm and quantitative cluster by K-means algorithm. A model-based hybrid feature extraction method is proposed by optimizing the weight matrix of the LSDA algorithm. The effectiveness of the suggested approach is demonstrated by preliminary experiments on the English video database. Experimental results demonstrate that the proposed optimized algorithm can increase recognition rate up to 97%, which is 18% higher than the original algorithm. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Lip segmentation using localized active contour model with automatic initial contour" published in the journal Neural Computing & Applications. The article has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate or irrelevant references, and not being in scope of the journal or guest-edited issue. The authors have not responded to correspondence from the publisher regarding this retraction. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

Lip-reading is one of important approaches for human-computer interaction (HCI). Its development would have a large range of applications, especially in augmented reality. Lip segmentation is the first and foremost step in the lip-reading system. Conventional method of region-based active contour model adopts the global information of image and is unable to perform well. In this paper, from a localized perspective, we introduce the methodology of localized active contour model (LACM) and, meanwhile, propose the method that using LACM to perform the lip segmentation with the initial contour automatically generated. The scope for active contour model is reduced to the local region that reduces the disturbances of unrelated factors. The experimental results demonstrate the method adopts this model would dramatically improve the robustness for lip segmentation. On this basis, we analyze the influence of initial contours and local radiuses, study the efficiencies under different initial contours and compare it with the conventional active contour model which adopts the global information. [ABSTRACT FROM AUTHOR]

Image quality is one of the most crucial influence factors when conducting biometric image-based human identification. However, it is not considered in most existing multi-biometric fusion algorithms. In this paper, a quality assessment-based dynamic weighted fusion algorithm is proposed, which applies a method of using image quality scores, and the scores are evaluated by integrating various image quality metrics to assess the quality of the feature matching process. According to the classification of the dual-modality feature matching quality, a dynamic weighted fusion strategy is proposed to increase the weight of biometric traits with better quality and weaken the impact of low-quality biometric traits on the recognition results, to achieve the adaptive optimization fusion of the biometric score level. Finally, the fused scores are used to make decisions. Experimental results reveal that the proposed algorithm is more robust and has better achievement than unimodal biometrics and traditional fusion algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour. [ABSTRACT FROM AUTHOR]