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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 The article has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate or irrelevant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]

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]

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]