Application Research of Face Feature Extraction Based on DM642

2010 ◽  
Vol 139-141 ◽  
pp. 2024-2028
Author(s):  
Jin Qing Liu ◽  
Qun Zhen Fan ◽  
Dong Cao
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Application Research ◽  
Feature Extraction Method ◽  
Modified Method ◽  
Face Images ◽  
Safety Awareness ◽  
Time Requirements ◽  
Hardware Platforms ◽  
Ica Algorithm

With the safety awareness strengthened, identification authentication technology has been increasingly concerned. Face recognition is attractive in pattern recognition and artificial intelligence field, and face feature extraction is a very important part in face recognition. This paper first introduced preprocessing of face images, PCA and ICA algorithm. Considering PCA and ICA their respective strengths and weaknesses, then a novel face feature extraction method based on PCA and ICA is proposed. The NN classifier is select to face classification and recognition on the ORL face database. From the actual requirements, the paper analyses hardware platforms based on DM642, and finally use tool CCS software to optimize program and implementation base on DM642 to meet the real-time requirements. Experiments indicated that the modified method is superior to PCA and ICA algorithm.

scholarly journals Research on Face Recognition Algorithm Based on Multi-Class Support Vector Machine

2021 ◽  
Vol 2078 (1) ◽  
pp. 012049
Author(s):  
Liuxun Xue ◽  
Hui Li ◽  
Ping Wang ◽  
Zhiyang Lin ◽  
Huanyu Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Face Recognition ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Machine Method ◽  
Feature Extraction Method ◽  
Main Research ◽  
Face Images

Abstract Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.

F-2DCCA: A New Fuzzy Feature Extraction Method for Face Recognition

2013 ◽  
Vol 12 (4) ◽  
pp. 823-828
Author(s):  
Yanyan Liu ◽  
Yibin Zhao ◽  
Jianguo Wang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Feature Extraction Method

An efficient deep learning model for classification of thermal face images

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Basma Abd El-Rahiem ◽  
Ahmed Sedik ◽  
Ghada M. El Banby ◽  
Hani M. Ibrahem ◽  
Mohamed Amin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Low Contrast ◽  
Content Type ◽  
Automatic Feature Extraction ◽  
Face Images ◽  
Proposed Model ◽  
Thermal Face ◽  
Deep Learning Model

PurposeThe objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.Design/methodology/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

An Improved Linear Discriminant Analysis Method and its Application to Face Recognition

2014 ◽  
Vol 556-562 ◽  
pp. 4825-4829 ◽  
Author(s):  
Kai Li ◽  
Peng Tang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extraction Method ◽  
Analysis Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Discriminant Analysis Method

Linear discriminant analysis (LDA) is an important feature extraction method. This paper proposes an improved linear discriminant analysis method, which redefines the within-class scatter matrix and introduces the normalized parameter to control the bias and variance of eigenvalues. In addition, it makes the between-class scatter matrix to weight and avoids the overlapping of neighboring class samples. Some experiments for the improved algorithm presented by us are performed on the ORL, FERET and YALE face databases, and it is compared with other commonly used methods. Experimental results show that the proposed algorithm is the effective.

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