Examination on face recognition method based on type 2 blurry

Abstract

In recent years, technology of face recognition has developed rapidly, more and more face recognition technologies have been integrated into our work and life. In practical applications, due to influence of various factors, the resolution of the face image is low, the noise interference is large, and the illumination changes sharply during the imaging process, which brings difficulties to the face recognition, which seriously affects the accuracy of the face recognition method. This paper aims to introduce two-type fuzzy theory into face recognition and study its extraction and recognition methods of face feature. Firstly, it introduce the face recognition technology simply. Face recognition is a technique that uses a computer to analyze a face image and extract valid identification information to identify the identity. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two methods for extracting features from face recognition. Principal Component Analysis (PCA) is a data analysis method that uses a small number of characterizations to reduce the number of dimensions, which reduces computational complexity greatly. The purpose of linear discriminant analysis is to extract data from high-dimensional feature spaces. Extracted the low-dimensional features with recognition ability, and studied the two-type fuzzy system based on fuzzy sets deeply. Obtained the output function of the two-type fuzzy system by studying the structure of each layer of the two-type fuzzy system. Introduce two types of fuzzy ideas into linear discriminant analysis. Discussed the construction of fuzzy membership functions, the selection of kernel functions and the determination of clustering rules. Finally, the ORL face database of the trained fuzzy face recognition model. As a result, the face recognition method based on the type 2 fuzzy has certain feasibility. The experimental results show that face recognition based on interval two-type fuzzy neural network has good recognition rate and anti-noise ability.

Keywords

Type 2 fuzzy rules linear identification face recognition feature extraction

Get full access to this article

View all access options for this article.

References

and Ding

, Robust biometrics-based authentication scheme for multiserver environment, IEEE Systems Journal 9(3) (2015), 816–823.

, Li

, Peng

and Yang

, A biometrics and smart cards-based authentication scheme for multi-server environments, Security & Communication Networks 8(17) (2015), 3219–3228.

Bindu

and Manjunathachari

, Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition, Sensor Review 38(3) (2018), 269–281.

Ibrayev

, Myrzakhan

, Krestinskaya

, Irmanova

and James

A.P.

, On-chip face recognition system design with memristive hierarchical temporal memory, Journal of Intelligent & Fuzzy Systems 34(3) (2017), 1393–1402.

Tan

, Cao

and Cai

M.C.M.

, A new system of face recognition: using fuzziness and sparsity, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23(6) (2015), 829–844.

Wang

K.C.

, Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition, Sensors 15(1) (2015), 1458–1478.

Aravinth

and Valarmathy

, Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication, Journal of Photographic Science 64(1) (2016), 1–14.

Zhang

De-xin

, an

Peng

and Zhang

Hao-xiang

, Application of robust face recognition in video surveillance systems, Optoelectronics Letters 14(2) (2018), 152–155.

Wang

, Yang

and Ben

, Low-resolution degradation face recognition over long distance based on cca, Neural Computing & Applications 26(7) (2015), 1645–1652.

10.

Sakulchit

, Kuzeljevic

and Goldman

R.D.

, Evaluation of digital face recognition technology for pain assessment in young children, Clinical Journal of Pain 35(1) (2019), 18–22.