Subject independent facial expression recognition with robust face detection using a convolutional neural network

Matsugu, Masakazu; Mori, Katsuhiko; Mitari, Yusuke; Kaneda, Yuji

doi:10.1016/s0893-6080(03)00115-1

Cited by 545 publications

(248 citation statements)

References 9 publications

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“…We now apply the concept on convolution neural networks to facial expression recognition [17] which is one the most difficult to understand due to the variation in facial appearances, postures, face sizes and translation and invariance. A large number of variables allow proper use of the convolution neural network and its learning capabilities.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Efficient Object Recognition using Convolution Neural Networks Theorem

Thakral¹,

Shekhar²,

Victor³

2017

IJCA

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Object recognition is the process of identification of an object in an image. There exist various algorithms for the same. Appearance based algorithms have demonstrated good efficiency, however, their performance gets affected adversely in the presence of clutter or when background changes are affected. We hope to overcome this issue by using Convolution Neural Network (CNN) Theorem. The approach is shape based and has been proven to work well under broad range of circumstances: varied lighting conditions, affine transformations, etc. It involves tiling, which is the phenomenon of the use of multiple layers of neurons to process small portions of the image, which are then used to obtain better representations of the image. This allows CNN to be translation-tolerant. The neural elements learn to recognize objects about which they have no previous information, this "learning" mechanism is affected by the fact that representations of the image are learned by the inner layers of the deep architectures of neurons. Unlike RBM and Auto-encoder, which are capable of learning only single global weight matrix layers, the CNN theorem makes use of shared weight in convolution layers, which means that the same filter (weight bank) is used for each pixel in the layer, which reduces the memory footprint and improves performance.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Efficient Object Recognition using Convolution Neural Networks Theorem

Thakral¹,

Shekhar²,

Victor³

2017

IJCA

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“…Matsugu, et al, [7] proposed face detection using convolutional neural network and facial expression recognition. They have used k modules to detect face.…”

Section: Recent Developmentsmentioning

confidence: 99%

A Novel Technique to Detect Faces in a Group Photo

Chandran¹

2012

IJCA

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Digital Image Processing plays a vital role in several applications. One of the applications is detection of people faces in a digital photo. Several research works have been progressing in the field of face identification and matching, facial expressions identification, smile detection, etc. Any application related to face, first step is detection of face. Thus, the face detection plays a key role. The face detection is a complicated process due to background objects, orientation, scale, lighting, geometrics, quality of image, occlusion, disguise etc. This paper discusses different research techniques published in the peer reviewed journal and proposed a technique to detect faces in a group photo. General TermsImage Processing.

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“…In the past decade, image classification has shown major advances in terms of classification accuracy. In recent times, image classification models are rapidly being used in various application fields, such as handwritten numeral recognition [2], recognition of traffic signs from roadside boards [3]- [5], segmentation of Magnetic Resonance Image (MRI) [6], identification of chest pathology [7], face detection from images [8] and so on. Existing models are categorized into unsupervised and supervised modes.…”

Section: Introductionmentioning

confidence: 99%

“…The unique characteristics of CNN such as weight sharing and preservation of the corresponding locality, which make the deep architecture the most suitable for 2D images to conserve a better epitome, are the outcome of using convolution and following subsampling layer. Right now, CNN based models are being used vastly in 2D material identification and various cases [3]- [8].…”

Section: Introductionmentioning

confidence: 99%

A Robust System for Noisy Image Classification Combining Denoising Autoencoder and Convolutional Neural Network

Roy¹,

Hossain²,

Akhand³

et al. 2018

ijacsa

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Abstract-Image classification, a complex perceptual task with many real life important applications, faces a major challenge in presence of noise. Noise degrades the performance of the classifiers and makes them less suitable in real life scenarios. To solve this issue, several researches have been conducted utilizing denoising autoencoder (DAE) to restore original images from noisy images and then Convolutional Neural Network (CNN) is used for classification. The existing models perform well only when the noise level present in the training set and test set are same or differs only a little. To fit a model in real life applications, it should be independent to level of noise. The aim of this study is to develop a robust image classification system which performs well at regular to massive noise levels. The proposed method first trains a DAE with low-level noise-injected images and a CNN with noiseless native images independently. Then it arranges these two trained models in three different combinational structures: CNN, DAE-CNN, and DAE-DAE-CNN to classify images corrupted with zero, regular and massive noises, accordingly. Final system outcome is chosen by applying the winner-takes-all combination on individual outcomes of the three structures. Although proposed system consists of three DAEs and three CNNs in different structure layers, the DAEs and CNNs are the copy of same DAE and CNN trained initially which makes it computationally efficient as well. In DAE-DAE-CNN, two identical DAEs are arranged in a cascaded structure to make the structure well suited for classifying massive noisy data while the DAE is trained with low noisy image data. The proposed method is tested with MNIST handwritten numeral dataset with different noise levels. Experimental results revealed the effectiveness of the proposed method showing better results than individual structures as well as the other related methods.

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Subject independent facial expression recognition with robust face detection using a convolutional neural network

Cited by 545 publications

References 9 publications

Efficient Object Recognition using Convolution Neural Networks Theorem

Efficient Object Recognition using Convolution Neural Networks Theorem

A Novel Technique to Detect Faces in a Group Photo

A Robust System for Noisy Image Classification Combining Denoising Autoencoder and Convolutional Neural Network

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