Applying Artificial Neural Networks for Face Recognition

Le, Thai Hoang

doi:10.1155/2011/673016

Cited by 66 publications

(23 citation statements)

References 24 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, [20] proposed a new feature learning algorithm called sparse filtering. In this paper, we choose Kmeans, Sparse Filtering and RICA [21]algorithms for comparison. The comparison results are shown in the experiments part.…”

Section: Pre-trainmentioning

confidence: 99%

Deep Learning for Intelligent Substation Device Infrared Fault Image Analysis

Lin¹,

Sun²,

Li³

et al. 2016

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract.As an important kind of data for device status evaluation, the increasing infrared image data in electrical system puts forward a new challenge to traditional manually processing mode. To overcome this problem, this paper proposes a feasible way to automatically process massive infrared fault images. We take advantage of the imaging characteristics of infrared fault images and detect fault regions together with its belonging device part by our proposed algorithm, which first segment images into superpixels, and then adopt the state-of-the-art convolutional and recursive neural network for intelligent object recognition. In the experiment, we compare several unsupervised pre-training methods considering the importance of a pretrain procedure, and discuss the proper parameters for the proposed network. The experimental results show the good performance of our algorithm, and its efficiency for infrared analysis.

show abstract

Section: Pre-trainmentioning

confidence: 99%

Deep Learning for Intelligent Substation Device Infrared Fault Image Analysis

Lin¹,

Sun²,

Li³

et al. 2016

MATEC Web of Conferences

View full text Add to dashboard Cite

show abstract

“…Artificial neural networks ANN algorithms are machine learning techniques that are designed as an approximation of human brain model. ANNs have been widely used to overcome signal processing challenges such as speaker identification, face recognition, and classification difficulties [15,16]. Researchers have proposed many types of artificial neural networks to recognize a human face [17].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method of Face Feature Extraction, Classification Based on MLBP and Layered-Recurrent Network

Al-Qaisi¹,

Al-Tarawneh²,

ElSaid³

et al. 2020

View full text Add to dashboard Cite

Face feature extraction and classification is an attracting research area for its various applications. This paper proposes a hybrid technique based on modified local binary pattern (MLBP) and Layered-Recurrent neural network (L-RNN) to recognize the human faces. The proposed MLBP algorithm reduces the dimensions of extracted face images features. The classification process is conducted using L-RNN. The quasi-Newton back propagation algorithm is used to train the L-RNN. The proposed hybrid technique is examined on MUCT database and the performance analysis of different ANN techniques shows that the Hybrid technique is robust and has superior performance over conventional methods. It achieves the best classification rate of 98%.

show abstract

“…Neural networks-based face recognition improved the results of all previous methods and also brought an increase in efficiency and execution time. A variety of reviews [6][7][8][9][10][11][12][13][14] compare the advantages, disadvantages and results of multiple different neural network methods. The reviews mark the importance of CNNs (convolutional neural networks) and deep learning in the area of facial recognition, deep learning specifically being considered a huge step in the evolution of facial recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation

2018

View full text Add to dashboard Cite

The detection of human emotions has applicability in various domains such as assisted living, health monitoring, domestic appliance control, crowd behavior tracking real time, and emotional security. The paper proposes a new system for emotion classification based on a generative adversarial network (GAN) classifier. The generative adversarial networks have been widely used for generating realistic images, but the classification capabilities have been vaguely exploited. One of the main advantages is that by using the generator, we can extend our testing dataset and add more variety to each of the seven emotion classes we try to identify. Thus, the novelty of our study consists in increasing the number of classes from N to 2N (in the learning phase) by considering real and fake emotions. Facial key points are obtained from real and generated facial images, and vectors connecting them with the facial center of gravity are used by the discriminator to classify the image as one of the 14 classes of interest (real and fake for seven emotions). As another contribution, real images from different emotional classes are used in the generation process unlike the classical GAN approach which generates images from simple noise arrays. By using the proposed method, our system can classify emotions in facial images regardless of gender, race, ethnicity, age and face rotation. An accuracy of 75.2% was obtained on 7000 real images (14,000, also considering the generated images) from multiple combined facial datasets.

show abstract

Applying Artificial Neural Networks for Face Recognition

Cited by 66 publications

References 24 publications

Deep Learning for Intelligent Substation Device Infrared Fault Image Analysis

Deep Learning for Intelligent Substation Device Infrared Fault Image Analysis

A Hybrid Method of Face Feature Extraction, Classification Based on MLBP and Layered-Recurrent Network

Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation

Contact Info

Product

Resources

About