A method of learning based boosting in multiple classifier for color facial expression identification

Bhakta, Dhananjoy; Sarker, Goutam

doi:10.1109/retis.2015.7232898

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…When these layers are stacked together, a complete CNN architecture is formed. [19,20,21,22,23,24] The convolution layer is used to produce the activation map for all the features by convolving or sliding a kernel or filter across every location of the pixel matrix of the input image. This layer is essential to find out which feature exists especially in which part of the image.…”

Section: Cnnmentioning

confidence: 99%

“…This layer is essential to find out which feature exists especially in which part of the image. [24,25,26,27,28] The convolutional layer of the CNN determines the output of neurons connected to local regions of input through the calculation of the scalar product between their weights and the regions connected to the input volume. The rectified linear unit (commonly shortened to ReLU) is used to apply an 'elementwise' activation function.…”

Section: Cnnmentioning

confidence: 99%

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A Convolution Neural Network for Optical Character Recognition and Subsequent Machine Translation

Sarker¹,

Ghosh²

2018

IJCA

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Optical character recognition has been a longstanding challenging research topic in the broad area of machine learning and pattern recognition. In the present paper, we investigate the problem of textual image recognition and translation, which is among the most daunting challenges in image-based sequence recognition. A convolutional neural network (CNN) architecture, which integrates optical character recognition and natural language translation into a unified framework, is proposed. The accuracy of both OCR output and subsequent translation is moderate and satisfactory. The proposed system for OCR and subsequent translation is an effective, efficient and most promising one.

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