Lip segmentation in color images

Rohani, Reza; Alizadeh, S.; Sobhanmanesh, Fariborz; Boostani, Reza

doi:10.1109/innovations.2008.4781689

Cited by 22 publications

(7 citation statements)

References 11 publications

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“…Lip image processing has attracted wide-spread research interest in recent years for its wide application in automatic visual speech recognition [1][2], visual speaker authentication [3][4][5], lip synchronization for facial animation [6], etc. Lip region segmentation, which is also referred to as lip segmentation, is the first and most crucial step in various lip-related applications [7].…”

Section: Introductionmentioning

confidence: 99%

Lip image segmentation based on a fuzzy convolutional neural network

Guan

Wang

Liew

2019

IEEE Trans. Fuzzy Syst.

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Research has shown that the human lip and its movements are a rich source of information related to speech content and speaker's identity. Lip image segmentation, as a fundamental step in many lipreading and visual speaker authentication systems, is of vital importance. Because of variations in lip color, lighting conditions and especially the complex appearance of an open mouth, accurate lip region segmentation is still a challenging task. To address this problem, this paper proposes a new fuzzy deep neural network having an architecture that integrates fuzzy units and traditional convolutional units. The convolutional units are used to extract discriminative features at different scales to provide comprehensive information for pixel-level lip segmentation. The fuzzy logic modules are employed to handle various kinds of uncertainties and to provide a more robust segmentation result. An end-to-end training scheme is then used to learn the optimal parameters for both the fuzzy and the convolutional units. A dataset containing more than 48,000 images of various speakers, under different lighting conditions, was used to evaluate lip segmentation performance. According to the experimental results, the proposed method achieves state-of-the-art performance when compared with other algorithms.

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Section: Introductionmentioning

confidence: 99%

Lip image segmentation based on a fuzzy convolutional neural network

Guan

Wang

Liew

2019

IEEE Trans. Fuzzy Syst.

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“…The red exclusion method [16] is widely used, due to its simplicity and efficiency. Similar to the approach in [20], [21] In [22], an approach is presented for a system based on lip reading. In [23] and [24], HMM based visual speech recognition is described.…”

Section: A Related Workmentioning

confidence: 99%

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

Travieso

Zhang

Miller

et al. 2014

Image and Vision Computing

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Abstract-In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8% 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

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“…The FCM method is widely used in biomedical engineering for detection of infarct lesions [44], detection of breast cancer [45,46], lungs nodule detection [47], analysis of lymph node sections [48], and other applications like ship detection in SAR images [49], road seed extraction [50], fire detection [51], face detection [52], lip segmentation [53], and proposing a polynomial-based neural network for pattern classification [54].…”

Section: Related Workmentioning

confidence: 99%

A target-based color space for sea target detection

2011

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Sea target detection is a vital application for military and navigation purposes. A new supervised clustering method based on the combination of the PSO and FCM techniques is presented for the sea target detection problem. The color components of the target and non-target pixels in the RGB color space are used as features to train the classification algorithm. The new classifier is presented in the form of a new color space which we call the Target-based Color Space (TCS); in fact the RGB color space is converted to this new space through a 3 × 3 matrix. The Particle Swarm Optimization (PSO) algorithm is then used to search for the optimum weights of the conversion matrix which results in a more discriminating clustering space between the target and non-target pixels. In other words, solving the optimization problem, minimization of the objective function of the FCM clustering technique in linear and quadratic transform domain (with a NP-hard problem in quadratic conversion), is done using the PSO algorithm. The main objective of this work is to demonstrate the efficiency of using just color features, as well as color space conversion in the classification domain. Experimental results show the efficiency of new method in finding sea targets in color images.

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Lip segmentation in color images

Cited by 22 publications

References 11 publications

Lip image segmentation based on a fuzzy convolutional neural network

Lip image segmentation based on a fuzzy convolutional neural network

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

A target-based color space for sea target detection

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