Handwritten recognition of Tamil vowels using deep learning

Prashanth, N Ram; Siddarth, B; Ganesh, Anirudh; Kumar, Vaegae Naveen

doi:10.1088/1757-899x/263/5/052035

Cited by 6 publications

(7 citation statements)

References 1 publication

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“…The results obtained in table 1 show that the proposed metrics to discriminate texture are efficient in face recognition (G g in (10) and G h in (11)). Moreover, we provide an additional set of experiments in general texture discrimination (e.g., texture Table 3.…”

Section: Resultsmentioning

confidence: 91%

“…Since geodesic distances are a natural dissimilarity metric for statistical distributions, we propose to calculate dissimilarities between distinct face images by summing dissimilarities between the texture of their corresponding landmarks which are given as geodesic distances approximations between multivariate normal distributions as presented in section 2. Considering L, the total number of landmarks in a landmark topology, a geodesic distance approximation between multivariate normal distributions can be adopted, i.e., G f (equation (4)), G g (equation (10)) or G h (equation (11)).…”

Section: Face Classificationmentioning

confidence: 99%

“…Moreover, the image processing and computer vision fields may be used to help to extract reliable features for several instrumentation-related applications which use texture information, such as face recognition [1] [2] [3] [4], brain image recognition [5] [6], texture recognition of material images [7], food image recognition [8] [9], character recognition [10] [11], yawning detection [12], etc. In this work, we are mainly interested in face recognition by using efficient texture dissimilarity metrics based on geodesic distance approximations between probability distributions.…”

Section: Introductionmentioning

confidence: 99%

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Face recognition based on geodesic distance approximations between multivariate normal distributions

Soldera

Dodson

Scharcanski

2017

2017 IEEE International Conference on Imaging Systems and Techniques (IST)

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Geodesic distance is a natural dissimilarity measure between probability distributions of a specific type, and can be used to discriminate texture in image-based measurements. Furthermore, since there is no known closed-form solution for the geodesic distance between general multivariate normal distributions, we propose two efficient approximations to be used as texture dissimilarity metrics in the context of face recognition. A novel face recognition approach based on texture discrimination in high-resolution color face images is proposed, unlike the typical appearance-based approach that relies on low-resolution grayscale face images. In our face recognition approach, sparse facial features are extracted using predefined landmark topologies, that identify discriminative image locations on the face images. Given this landmark topology, the dissimilarity between distinct face images are scored in terms of the dissimilarities between their corresponding face landmarks, and the texture in each one of these landmarks is represented by multivariate normal distributions, expressing the color distribution in the vicinity of each landmark location. The classification of new face image samples occurs by determining the face image sample in the training set which minimizes the dissimilarity score, using the nearest neighbor rule. The proposed face recognition method was compared to methods representative of the state-of-the-art, using color or grayscale face images, and presented higher recognition rates. Moreover, the proposed texture dissimilarity metric also is efficient in general texture discrimination (e.g., texture recognition of material images), as our experiments suggest.

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Section: Resultsmentioning

confidence: 91%

Section: Face Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Face recognition based on geodesic distance approximations between multivariate normal distributions

Soldera

Dodson

Scharcanski

2017

2017 IEEE International Conference on Imaging Systems and Techniques (IST)

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show abstract

“…Considering L, the total number of landmarks in a landmark topology, a geodesic distance approximation between multivariate normal distributions can be adopted, i.e. G f (equation (4)), G g (equation (10)) or G h (equation (11)).…”

Section: Face Classificationmentioning

confidence: 99%

“…Moreover, the image processing and computer vision fields may be used to help to extract reliable features for several instrumentation-related applications which use texture information, such as face recognition [1][2][3][4], brain image recognition [5,6], texture recognition of material images [7], food image recognition [8,9], character recognition [10,11], yawning detection [12], etc. In this work, we are mainly interested in face recognition by using efficient texture dissimilarity metrics based on geodesic distance approximations between probability distributions.…”

Section: Introductionmentioning

confidence: 99%

Face recognition based on texture information and geodesic distance approximations between multivariate normal distributions

Dodson¹,

Soldera²,

Scharcanski³

2018

Meas. Sci. Technol.

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Geodesic distance is a natural dissimilarity measure between probability distributions of a specific type, and can be used to discriminate texture in image-based measurements. Furthermore, since there is no known closed-form solution for the geodesic distance between general multivariate normal distributions, we propose two efficient approximations to be used as texture dissimilarity metrics in the context of face recognition. A novel face recognition approach based on texture discrimination in high-resolution color face images is proposed, unlike the typical appearance-based approach that relies on low-resolution grayscale face images. In our face recognition approach, sparse facial features are extracted using predefined landmark topologies, that identify discriminative image locations on the face images. Given this landmark topology, the dissimilarity between distinct face images are scored in terms of the dissimilarities between their corresponding face landmarks, and the texture in each one of these landmarks is represented by multivariate normal distributions, expressing the color distribution in the vicinity of each landmark location. The classification of new face image samples occurs by determining the face image sample in the training set which minimizes the dissimilarity score, using the nearest neighbor rule. The proposed face recognition method was compared to methods representative of the state-of-the-art, using color or grayscale face images, and presented higher recognition rates. Moreover, the proposed texture dissimilarity metric also is efficient in general texture discrimination (e.g. texture recognition of material images), as our experiments suggest.

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Recurrent Neural Network-Based Character Recognition System for Tamil Palm Leaf Manuscript Using Stroke Zoning

Athisayamani

Singh

Kumar

2020

Lecture Notes in Networks and Systems

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Handwritten recognition of Tamil vowels using deep learning

Cited by 6 publications

References 1 publication

Face recognition based on geodesic distance approximations between multivariate normal distributions

Face recognition based on geodesic distance approximations between multivariate normal distributions

Face recognition based on texture information and geodesic distance approximations between multivariate normal distributions

Recurrent Neural Network-Based Character Recognition System for Tamil Palm Leaf Manuscript Using Stroke Zoning

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