Abstract
In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.
Highlights
Biometric identification systems are widely used in security-critical systems [1,2,3] and man-machine interfaces (MMI) [4]
Experimental results show that the Local Cross Pattern (LCP) descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods
The accuracy of the Face 94 database obtained by the proposed fuzzy wavelet domain (FWD)-based method is shown in and 100%, 99.7%, 99.5%, and 98.8% accuracy rates were achieved after using the fuzzy wavelet and pixel domains, respectively
Summary
Biometric identification systems are widely used in security-critical systems [1,2,3] and man-machine interfaces (MMI) [4]. They are used for personnel control and criminal monitoring/detection in areas such as military, hospital, airport, education [3,5,6,7]. People tend to hide themselves with criminal identification This tendency complicates the task of criminal identification [5,6,7]. In such cases, recognition can be achieved with camera images taken in the outdoors, where the targeted person is unaware.
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