Face Description with Local Binary Patterns and Local Ternary Patterns: Improving Face Recognition Performance Using Similarity Feature-Based Selection and Classification Algorithm

2014 ◽  
Author(s):  
Chi Kien Tran ◽  
Tsair Fwu Lee ◽  
Liyun Chang ◽  
Pei Ju Chao
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Local Binary Patterns ◽  
Classification Algorithm ◽  
Local Ternary Patterns ◽  
Feature Based

LINEAR AND NONLINEAR FEATURE-BASED FUSION ALGORITHMS FOR FACE RECOGNITION

2006 ◽  
Vol 04 (04) ◽  
pp. 659-676
Author(s):  
JIAN HUANG ◽  
PONGCHI YUEN ◽  
WEN-SHENG CHEN ◽  
JIANHUANG LAI ◽  
XINGE YOU
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Recognition Algorithms ◽  
Face Recognition System ◽  
Combination Scheme ◽  
Feature Based ◽  
Linear And Nonlinear ◽  
Nonlinear Feature ◽  
Weighted Combination

Integration of various face recognition algorithms has proved to be a feasible approach to improve the performance of a face recognition system. Different face recognition algorithms are often based on different representations of the input patterns or on extracted features and hence may complement each other. Linear and nonlinear feature based algorithms can capture and handle different kinds of variations, such as pose, illumination and expression variations. To make full use of the different advantages of different classifiers, we propose combining four linear and nonlinear face recognition algorithms via a weighted combination scheme to improve the recognition performance of a face recognition system. The FERET, YaleB and CMU PIE database are used for evaluating the combination scheme and the results confirm the effectiveness of the proposed combination scheme.

Study of Face Feature Extraction and Recognition Techniques Based on Wavelet Analysis

2014 ◽  
Vol 1030-1032 ◽  
pp. 1810-1813
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Face Recognition ◽  
Wavelet Decomposition ◽  
Feature Vector ◽  
Recognition Performance ◽  
Image Database ◽  
Face Image ◽  
Support Vector ◽  
Svm Model ◽  
Feature Based ◽  
Vector Dimension

Face recognition is a research hotspot of pattern recognition and artificial intelligence. This paper presents a method of extract face feature based on Wavelet. First, reduce vector dimension by wavelet decomposition of the image, second, train the multi class support vector machine (SVM) model by face feature vector extracted and make face recognition finally. The experiments on ORL face image database of the algorithm shows the superiority of the proposed algorithm in terms of recognition performance.

Face Perception in Face Transplant Patients

2019 ◽  
Vol 35 (05) ◽  
pp. 525-533
Author(s):  
Evrim Gülbetekin ◽  
Seda Bayraktar ◽  
Özlenen Özkan ◽  
Hilmi Uysal ◽  
Ömer Özkan
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Face Processing ◽  
Recognition Performance ◽  
Discrimination Performance ◽  
Object Discrimination ◽  
Face Transplantation ◽  
Transplant Patients ◽  
Facial Surgery ◽  
The Face

AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.

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