Face Recognition System Invariant to Expression

Varma, Rahul; Gupta, Sandesh; Gupta, Phalguni

doi:10.1007/978-3-319-09333-8_33

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 2 shows the percentage recognition rate of individual ARLBP and FDCT technique and hybrid technique [FDCT +ARLBP] algorithm compared with the existing algorithm [27] for FERET database with image size of 130x150 with different descriptors and distance measures. Further, the proposed work is also compared with the existing algorithms for the NIR database [30] and FERET database [28,29,31,32,33]…”

Section: Number Of Genuine Samples Recognition Ratementioning

confidence: 99%

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

K¹,

Ravi²

2021

IJCNIS

View full text Add to dashboard Cite

Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

show abstract

Section: Number Of Genuine Samples Recognition Ratementioning

confidence: 99%

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

K¹,

Ravi²

2021

IJCNIS

View full text Add to dashboard Cite

show abstract

Frame Work for Expression Invariant Face Recognition System Using Warping Technique

Ahlawat¹,

Bpsmv²,

Nehra³

et al. 2018

IJIGSP

View full text Add to dashboard Cite

Facial expressions, usually has an adverse effect on the performance of a face recognition system. In this investigation, expression invariant face recognition algorithm is presented that converts input face image with an arbitrary expression into its corresponding neutral facial image. In the present study, deep learning algorithm is used to train classifiers for reference keypoints, where key-points are located and deep neural network is trained to make the system able to locate the landmarks in test image. Create an intermediate triangular mesh from the test and reference image and then warp it using affine transform and take the average of the normalized faces. To extract the features presented in the result image shift invariant feature extraction technique is used. Finally, results are compared and the recognition accuracy is determined for different expressions. The present work is tested on three different databases: JAFFE, Cohn-Kanade (CK) and Yale database. Experimental results show that the expression invariant face recognition method is very robust to variety of expressions and recognition accuracy is found to be 97.8 %, 96.8% and 95.7% for CK, JAFFE and Yale databases respectively.

show abstract

Face Recognition System Invariant to Expression

Cited by 2 publications

References 11 publications

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Frame Work for Expression Invariant Face Recognition System Using Warping Technique

Contact Info

Product

Resources

About