Image Classification with Convolutional Neural Networks

Ramos-Michel, Alfonso; Pérez‐Cisneros, Marco; Cuevas, Erik; Zaldívar, Daniel

doi:10.1007/978-3-030-70542-8_18

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…This paper focuses on the study of convolutional neural networks (CNN) and the creation of tests that make it easier to comprehend the impacts of their fundamental parameters. This is due to the broad variety of algorithms and methodologies available for performing element classification [2] [3].…”

Section: Introductionmentioning

confidence: 99%

“…Gender, race, and emotional expression on the face were all controlled in their model. The Convolutional Neural Network (CNN) has demonstrated significant potential in image processing, according to Ramos-Michel et al [2]. A CNN is composed of convolutional, maximum pooling, activation, batch normalization, drop out, and fully linked layers.…”

Section: Introductionmentioning

confidence: 99%

“…The training process is sped up by all of these layers. Different pooling techniques, including as average pooling and max pooling, are used to downsample the inputs and aid in generalization [2] [1]. Dropout, regularization, and data augmentation are employed to prevent overfitting.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictions of Criminal Tendency Through Facial Expression Using Convolutional Neural Network

James,

Okafor,

Chukwu

et al. 2024

J. Inf. Syst. Informatics

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Criminal intention is a critical aspect of human interaction in the 21st-century digital age where insecurity is on the high side as a major global threat. Kidnapping, killings, molestation of all sorts, gender-based violence, terrorism, and banditry are the trends of criminality in our nation, as such, there is a need to effectively explore innovative means to identify and cope with this evil menace in our society. The facial positioning of humans can tell their evil intention even if they pretended to smile with the evil in their minds. In normal instances, it may be very difficult to predict the heart of man, but with the trending information technology like image processing, the state of a human face could be used as a means to read their tendencies. This paper proposes a deep learning model based on the FER2013 dataset through the implementation of a CNN model that predicts criminal tendencies with the help of facial expressions. With this goal in mind, we explore a new level of image processing to infer criminal tendency from facial images through a convolutional neural network (CNN) deep learning algorithm in other to discriminate between criminal and non-criminal facial images. It was observed that CNN was more consistent in learning to reach its best test accuracy of 90.6%, which contained 8 convolutional layers. To increase the accuracy of this model, several procedures were explored using Random Search from the Keras tuner library, testing out various numbers of convolutional layers and Adam optimizer. It was also noticed that applying the dissection and visualization of the convolutional layers in CNN reveals that the shape of the face, eyebrows, eyeball, pupils, nostrils, and lips are taken advantage of by CNN to classify the images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%