A face recognition method based on a combination of integrated neural network and KICA algorithm

2012 ◽  
Author(s):  
Liangliang Gao ◽  
Shuang Hu ◽  
Zhaohui Li
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition Method

Face Recognition Method Based on the Fuzzy Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 134-137
Author(s):  
Xiang Pan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Fuzzy Neural Network ◽  
Experimental Results ◽  
Fuzzy Rules ◽  
Good Effect ◽  
Recognition Method ◽  
Fuzzy Neural ◽  
Artificial Neural

This paper discusses a face recognition method based on the fuzzy neural network (FNN). The fuzzy neural network has more advantages than artificial neural network alone. The paper firstly introduces the structure of the FNN. Than proposed the fuzzy rules and the study algorithm. Thirdly it researches on the process of face recognition. The experimental results prove that this method can achieve good location performance and good effect of extraction.

scholarly journals Emotion Recognition using Feed Forward Neural Network & Naïve Bayes

2019 ◽  
Vol 9 (2) ◽  
pp. 2487-2491
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Training Data ◽  
Recognition Method ◽  
Feed Forward Neural Network ◽  
Emotion Analysis ◽  
Software Application ◽  
Security Applications ◽  
Testing Data ◽  
Tone Of Voice

In this paper we analyze and predict the emotion of a user by recognizing his/her face. Face recognition is a software application which is used to identify a particular person; it will be mostly useful in security applications to secure our data. Now a day we are using face unlock in mobiles to unlock our phones. We need to know the emotions of a person in some situations. Though we can recognize his emotion through his tone of voice, it would be more helpful if get to know his emotions. This can be much helpful in finding out a criminal by finding out his emotion whether he is feeling nervous or not which expresses his/her fear by this. In order to analyze his/her emotion firstly we need to recognize his/her face, so we need to use face recognition method and then implement emotion analysis. Here we use different algorithms to implement emotion analysis such as CNN. We will have the dataset with pixels and emotion this will be the training data. Then we will be initially taking the picture and then convert them to pixels these will be acting as the testing data. We then use an algorithm to predict these pixels emotion which is nothing but predicting the emotion of the picture taken

scholarly journals An effective face recognition method using guided image filter and convolutional neural network

2021 ◽  
Vol 23 (3) ◽  
pp. 1699
Author(s):  
Yallamandaiah S. ◽  
Purnachand N.
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition Method ◽  
Image Filter ◽  
Guided Image Filter ◽  
Face Region ◽  
The Face ◽  
Recognition Systems

<p>In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the features and recognize the faces. The experiments were performed on face databases like ORL, JAFFE, and YALE and attained a recognition rate of 98.33%, 99.53%, and 98.65% respectively. The experimental results show that the suggested face recognition method attains good results than some of the state-of-the-art techniques.</p>

Face Recognition Based on Wavelet Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 216-219
Author(s):  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Face ◽  
Simulation Testing

It's well known that the technology of human face recognition has become a hot topicin pattern recognition field. Though a lot of progress has been made by many researchersthese years, many key problems still have to be solved in order to popularize the application of face recognition because of the complexity of face recognition. The background, development and main methods of face recognition are introducedfirstly in this paper, then a face recognition method which is based on wavelet transform,KL transform and BP neural networks is used in the paper.Here the face feature extraction includes wavelet transform and KL transform.Moreover,the recognition classifier is BP neural networks.The simulation testing in the paper holds good recognition rate.

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