Automatic Face recognition using neural network-PCA

Boualleg, A.; Bencheriet, Chemesse ennehar; Tebbikh, Hicham

doi:10.1109/ictta.2006.1684683

Cited by 8 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: B) Experimental Resultsmentioning

confidence: 99%

“…Additionally, we also plot the feature distributions to better visualize the results via principle component analysis (PCA). PCA is a widely used approach in which a linear transformation is designed to compress the information among features into the relatively lower dimensions [55][56][57][58][59]. Figures 4 and 5 show the distribution of the first and second principal component among all data sets for acoustic signals and medical record, respectively.…”

Section: B) Experimental Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Combining acoustic signals and medical records to improve pathological voice classification

Fang

Wang

Chen

et al. 2019

SIP

View full text Add to dashboard Cite

This study proposes two multimodal frameworks to classify pathological voice samples by combining acoustic signals and medical records. In the first framework, acoustic signals are transformed into static supervectors via Gaussian mixture models; then, a deep neural network (DNN) combines the supervectors with the medical record and classifies the voice signals. In the second framework, both acoustic features and medical data are processed through first-stage DNNs individually; then, a second-stage DNN combines the outputs of the first-stage DNNs and performs classification. Voice samples were recorded in a specific voice clinic of a tertiary teaching hospital, including three common categories of vocal diseases, i.e. glottic neoplasm, phonotraumatic lesions, and vocal paralysis. Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall (UAR) improvements of 2.02–10.32% and 2.48–17.31%, respectively, compared with systems that use only acoustic signals or medical records. The proposed algorithm also provides higher accuracy and UAR than traditional feature-based and model-based combination methods.

show abstract

Section: B) Experimental Resultsmentioning

confidence: 99%

Section: B) Experimental Resultsmentioning

confidence: 99%

Combining acoustic signals and medical records to improve pathological voice classification

Fang

Wang

Chen

et al. 2019

SIP

View full text Add to dashboard Cite

show abstract

“…The basic approach is to compute the eigenvectors of the covariance matrix of the original data, and to approximate it by making a linear combination of the leading eigenvectors [17]. By using the PCA procedure, the test vector can be identified by first, projecting the image onto the eigenvector space to obtain the corresponding set of weights, and then by comparing it with the set of weights for the vectors in the training set [18,19]. The problem of low-dimensional feature representation can be stated as follows: Let X= (x 1 , x 2 ,…, x i ,…, x n ) represents the n × N data matrix, where each x i is a vector of dimension n, concatenated from a face and online signature feature vectors.…”

Section: Principal Component Analysismentioning

confidence: 99%

Multimodal Biometric Using a Hierarchical Fusion of a Person's Face, Voice, and Online Signature

Elmir¹,

Elberrichi²,

Adjoudj³

2014

Journal of Information Processing Systems

View full text Add to dashboard Cite

Abstract-Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

show abstract

“…In [2], PCA is a useful analytical technique that has established application in fields such as face recognition and minimisation of image size, and is a prevalent technique to find patterns of high dimension data. It is a way of recognising patterns in data, and demonstrates the data in a way to pinnacle their affinities and variances.…”

Section: Principal Component Analysismentioning

confidence: 99%

Neural Network based Face Recognition System using Discrete Cosine Transform and Principal Component Analysis

Rajivegandhi¹,

Sabarinathan²,

Abinaya³

2014

IJCA

View full text Add to dashboard Cite

This paper deals with the implementation of Neural Network based face recognition system. As we know that face recognition system is one of the biometric information processing which has speed up in the last few decades. The developed algorithm for the face recognition system originates an image based approach, which uses the Two-Dimensional Discrete Cosine Transform (2D-DCT) to compress image, and then Self Organizing Map (SOM) Neural Network to recognize the face and its simulated in MATLAB. With the help of 2D-DCT the image vectors are extracted and these vectors sends to the neural network classifier which is developed using self organizing map, algorithm to recognize trained faces, faces with variations in expressions, changes of illumination, upto certain degrees. The alternate way of the same face recognition system is developed with the help of principle component analysis (PCA) instead of Two Dimensional Cosine Transform and Self-Organizing Map Neural Network to recognize the faces. In this proposed algorithm we use unsupervised single neural network as a classifier for both Two Dimensional Discrete Cosine Transform (2D-DCT) and Principal Component Analysis (PCA).

show abstract

Automatic Face recognition using neural network-PCA

Cited by 8 publications

References 7 publications

Combining acoustic signals and medical records to improve pathological voice classification

Combining acoustic signals and medical records to improve pathological voice classification

Multimodal Biometric Using a Hierarchical Fusion of a Person's Face, Voice, and Online Signature

Neural Network based Face Recognition System using Discrete Cosine Transform and Principal Component Analysis

Contact Info

Product

Resources

About