Dual space based face recognition using feature fusion

2006 ◽  
Author(s):  
A. Patra ◽  
S. Das
Keyword(s):  
Face Recognition ◽  
Dual Space ◽  
Feature Fusion

scholarly journals Deep Heterogeneous Feature Fusion for Template-Based Face Recognition

2017 ◽  
Author(s):  
Navaneeth Bodla ◽  
Jingxiao Zheng ◽  
Hongyu Xu ◽  
Jun-Cheng Chen ◽  
Carlos Castillo ◽  
...  
Keyword(s):  
Face Recognition ◽  
Feature Fusion ◽  
Heterogeneous Feature

scholarly journals AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED FEATURES FUSION SYSTEM

2013 ◽  
Vol 8 (2) ◽  
pp. 787-795
Author(s):  
Sasi Kumar Balasundaram ◽  
J. Umadevi ◽  
B. Sankara Gomathi
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Feature Fusion ◽  
Recognition Performance ◽  
Feature Selection Method ◽  
Recognition Method ◽  
Color Feature ◽  
Color Component ◽  
Pose Variation ◽  
Color Face Recognition

This paper aims to achieve the best color face recognition performance. The newly introduced feature selection method takes advantage of novel learning which is used to find the optimal set of color-component features for the purpose of achieving the best face recognition result. The proposed color face recognition method consists of two parts namely color-component feature selection with boosting and color face recognition solution using selected color component features. This method is better than existing color face recognition methods with illumination, pose variation and low resolution face images. This system is based on the selection of the best color component features from various color models using the novel boosting learning framework. These selected color component features are then combined into a single concatenated color feature using weighted feature fusion. The effectiveness of color face recognition method has been successfully evaluated by the public face databases.

scholarly journals Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
P. Drozdowski ◽  
F. Stockhardt ◽  
C. Rathgeb ◽  
D. Osorio-Roig ◽  
C. Busch
Keyword(s):  
Face Recognition ◽  
Privacy Protection ◽  
Feature Fusion ◽  
Biometric Data ◽  
Data Application ◽  
Indexing And Retrieval ◽  
Fusion Methods

scholarly journals Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique

2019 ◽  
Vol 7 (8) ◽  
pp. 19-22
Author(s):  
Prasad A. Jagdale ◽  
Sudeep D. Thepade
Keyword(s):  
Face Recognition ◽  
Feature Fusion ◽  
Recognition System ◽  
Security System ◽  
Major Drawback ◽  
Liveness Detection ◽  
Face Images ◽  
Face Recognition System ◽  
Confidential Data ◽  
Finger Recognition

Nowadays the system which holds private and confidential data are being protected using biometric password such as finger recognition, voice recognition, eyries and face recognition. Face recognition match the current user face with faces present in the database of that security system and it has one major drawback that it never works better if it doesn’t have liveness detection. These face recognition system can be spoofed using various traits. Spoofing is accessing a system software or data by harming the biometric recognition security system. These biometric systems can be easily attacked by spoofs like peoples face images, masks and videos which are easily available from social media. The proposed work mainly focused on detecting the spoofing attack by training the system. Spoofing methods like photo, mask or video image can be easily identified by this method. This paper proposed a fusion technique where different features of an image are combining together so that it can give best accuracy in terms of distinguish between spoof and live face. Also a comparative study is done of machine learning classifiers to find out which classifiers gives best accuracy.

scholarly journals Face Recognition Based on Discriminative Dictionary with Multilevel Feature Fusion

2014 ◽  
pp. 252-263
Author(s):  
Hongjun Li ◽  
Nicola Nobile ◽  
Ching Y. Suen
Keyword(s):  
Face Recognition ◽  
Feature Fusion

Improving face recognition using deep autoencoders and feature fusion

2022 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Ahmed Bouridane ◽  
Rafik Djemili ◽  
Ali Khider ◽  
Richard Jiang
Keyword(s):  
Face Recognition ◽  
Feature Fusion

A Convolutional Neural Network Based on Feature Fusion for Face Recognition

2020 ◽  
Vol 57 (10) ◽  
pp. 101508 ◽  
Author(s):  
王嘉欣 Wang Jiaxin ◽  
雷志春 Lei Zhichun
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Feature Fusion

scholarly journals DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Cheng ◽  
Hongfei Wang ◽  
Jingmei Zhou ◽  
Hui Chang ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Feature Fusion ◽  
Texture Features ◽  
Replay Attack ◽  
Attention Network ◽  
Detection Failure ◽  
The Face ◽  
Recognition Systems ◽  
Common Face

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

Feature Fusion Based Face Recognition Using EFM

2004 ◽  
pp. 643-650 ◽  
Author(s):  
Dake Zhou ◽  
Xin Yang
Keyword(s):  
Face Recognition ◽  
Feature Fusion
Sign in / Sign up

Export Citation Format

Share Document