Multimodal Priority Verification of Face and Speech Using Momentum Back-Propagation Neural Network

Park, Changhan; Ki, Myungseok; Jae-Chan, Namkung; Paik, Joonki

doi:10.1007/11760023_22

Cited by 15 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Huang and Shimizu (Huang & Shimizu, 2006) proposed using two neural networks whose outputs are combined to make a final decision on classifying a face. Park et al (Park et al, 2006) used a momentum back propagation neural network for face and speech verification. Many more face recognition methods that use artificial intelligence are emerging continually; however, one particular method; namely Intelligent Global Face Recognition, will be studied in this chapter, and is therefore presented in the following section.…”

Section: Face Recognition Methodsmentioning

confidence: 99%

Intelligent Global Face Recognition

Khashman¹

2007

Face Recognition

View full text Add to dashboard Cite

Section: Face Recognition Methodsmentioning

confidence: 99%

Intelligent Global Face Recognition

Khashman¹

2007

Face Recognition

View full text Add to dashboard Cite

“…A radial basis function neural network integrated with a non-negative matrix factorisation to recognise faces is presented in [39]. Moreover, for face and speech verifications, [40] utilise a momentum back propagation neural network. Non-negative sparse coding method to learning facial features using different distance metrics and normalised cross-correlation for face recognition is applied in [41].…”

Section: Artificial Neural Network In Face Recognitionmentioning

confidence: 99%

Face Recognition: Issues, Methods and Alternative Applications

Wójcik¹,

Gromaszek²,

Junisbekov³

2016

Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods

View full text Add to dashboard Cite

Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition.

show abstract

“…The work can be extended to clustering techniques like segmentation for the lower training times and higher performance. Since the training data is still images, there is more dependency on the image data like lighting, illumination conditions, poses of the faces, variations in expression and gender of the person also [18].…”

Section: Pso Coefficients With Emotional Back Propagation Neural mentioning

confidence: 99%

“…The input data can be static images or video sequences. The classified facial expressions are then represented in different methods and the performance is evaluated using different measurements [18]. The proposed architecture in this work contains the following stages: pre-processing of input images, feature extraction, training, classification, and database [6].…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Classification with Enhanced Feature Extraction using Particle Swarm Optimization and Emotional Back Propagation Neural Network

2016

IJCCIE

View full text Add to dashboard Cite

Human face-to-face communication plays an important role in human communication and interaction. In recent years, several different approaches have been proposed for developing methods of automatic facial expression analysis. In this paper we have proposed a novel facial expression recognition system which chooses the optimized features using particle swarm optimization (PSO) from the features calculated from principle component analysis (PCA) of input face images. These optimized features are then used to train the emotional backpropagation neural network (EBPNN). Using this neural network classifier, emotions are classified. The proposed architecture yields good results when PSO features compared with normal PCA features.

show abstract

Multimodal Priority Verification of Face and Speech Using Momentum Back-Propagation Neural Network

Cited by 15 publications

References 8 publications

Intelligent Global Face Recognition

Intelligent Global Face Recognition

Face Recognition: Issues, Methods and Alternative Applications

Facial Expression Classification with Enhanced Feature Extraction using Particle Swarm Optimization and Emotional Back Propagation Neural Network

Contact Info

Product

Resources

About