Image Fusion using Mallat Algorithm of Wavelet Transform

Enireddy, Vamsidhar; Karthikeyan, C; Manikandan, J

doi:10.35940/ijitee.k2040.0981119

Cited by 2 publications

(1 citation statement)

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“…Classification of the images are done using different Data Mining techniques [13,16,17,18], Neural networks, RNN, and also optimization of the networks are also done in order to improve the accuracy of the classification of Images [14,15]. With the increase use of machine learning and it is used in the different applications and KNN, Random Forest are some algorithms used in various applications [19,20]. There is a large increase in the technology that has improved the classification of images and also the recognization of images is a part in it.…”

Section: Introductionmentioning

confidence: 99%

Facial expression detection of all emotions and face recognition system

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2019

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Nowadays we have seen that the study of facial expression recognition became most important. In humans, the way of interaction will possible through text and voice. Apart from that, facial expressions play an important role in smooth communication among individuals. We can recognize the emotion of a human, based on facial expressions. First of all, facial areas were removed from the original images. Meanwhile, by using the different types of optimizers we have trained the convolutional neural network mainly focused on the face extraction. The accuracy of other methods like 2D PCA is 78.61%, Adaboost is 83.12%, SVM is 87.15%. In this paper, the method we proposed is softmax classifier by which we obtained an accuracy of 94.82% which is the highest among all. Here this softmax classifier we have used in this experiment, suggests the combination of face detection and emotion detection using CNN.

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