On the use of Image Enhancement Technique towards Robust Sclera Segmentation

Maheshan, M. S.; Harish, B. S.; Nagadarshan, N.

doi:10.1016/j.procs.2018.10.419

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…e objective of HE is to determine the frequency of dark colors that usually extends in the range of 0-50 pixels. e CLAHE, on the other hand, sets a contrast limit that gives proportionate white balance for the selected image [92].…”

Section: Histogram and Filtering Methodmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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Section: Histogram and Filtering Methodmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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“…Histogram equalization and contrast-limited adaptive histogram equalization approaches were used by Maheshan [1] to visualize blurred sclera. Further segmentation was performed using the K-means and fuzzy C-means algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

An effective model for the iris regional characteristics and classification using deep learning alex network

Thiyaneswaran¹,

Sengottaiyan²,

Kulandairaj

et al. 2022

IET Image Processing

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Iris biometrics is one of the fastest-growing technologies, and it has received a lot of attention from the community. Iris-biometric-based human recognition does not require contact with the human body. Iris is a combination of crypts, wolflin nodules, concentrated furrows, and pigment spots. The existing methods feed the eye image into deep learning network which result in improper iris features and certainly reduce the accuracy. This research study proposes a model to feed preprocessed accurate iris boundary into Alexnet deep learning neural network-based system for classification. The pupil centre and boundary are initially recorded and identified from the given eye images. The iris boundary and the centre are then compared for the identification using the reference pupil centre and boundary. The iris portion, exclusive feature of the pupil area is segmented using the parameters of multiple left-right point (MLRP) algorithms. The Alexnet deep learning multilayer networks 23, 24, and 25 are replaced according to the segmented iris classes. The remaining Alexnet layers are trained using the square gradient decay factor (GDF) in accordance with the iris features. The trained Alexnet iris is validated using suitable classes. The proposed system classifies the iris with an accuracy of 99.1%. The sensitivity, specificity, and F1-score of the proposed system are 99.68%, 98.36%, and 0.995. The experimental results show that the proposed model has advantages over current models.

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“…A escleraé a região externa branca e opaca dos olhos e apresenta vasos sanguíneos que podem ser usados no reconhecimento biométrico. A segmentação da escleraé a primeira etapa para o reconhecimento ocular, seu desempenho depende da iluminação e das múltiplas direções do olhar que influenciam diretamente no resultado esperado (Maheshan et al, 2018).…”

Section: Segmentação Da Escleraunclassified

Avaliação Automática do Alinhamento Ocular para Detecção do Estrabismo Usando Redes YOLO e U-net

Simões

Almeida

Silva

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Strabismus is an ophthalmological disease characterized by an imbalance in the ocular muscles, so that the two eyes do not fix the same point or object at the same time. Its incidence can occur from the first months of life to adulthood. Late detection may cause double vision or loss of vision, hence the importance of early detection of strabismus for its prognosis and effective treatment. That said, in this work is proposed a new method that uses neural networks to automatically detect strabismus by evaluating the positioning of the limbus center in relation to the center of the corners of the eyes. To do so, we use techniques of image processing and machine learning, obtaining sensitivity of 66,7%, specificity of 60% and accuracy of 65,6%. Resumo: O estrabismoé uma doença oftalmológica caracterizada por um desequilíbrio nos músculos oculares, fazendo com que os dois olhos não fixem o mesmo ponto ou objeto ao mesmo tempo. Sua incidência pode ocorrer desde os primeiros meses de vida até a idade adulta. A detecção tardia pode causar visão dupla ou perda da visão, por isso a importância da detecção precoce do estrabismo para seu prognóstico e tratamento eficaz. Dito isso, neste trabalhoé proposto um novo método que utiliza redes neurais para detectar automaticamente o estrabismo avaliando o posicionamento do centro do limbo em relação ao centro dos cantos dos olhos. Para tanto, utiliza-se técnicas de processamento de imagens e aprendizado de máquina, obtendo sensibilidade de 66,7%, especificidade de 60% e acurácia de 65,6%.

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On the use of Image Enhancement Technique towards Robust Sclera Segmentation

Cited by 15 publications

References 20 publications

Iris Recognition Development Techniques: A Comprehensive Review

Iris Recognition Development Techniques: A Comprehensive Review

An effective model for the iris regional characteristics and classification using deep learning alex network

Avaliação Automática do Alinhamento Ocular para Detecção do Estrabismo Usando Redes YOLO e U-net

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