Face Recognition Based on SWT, DCT and LTP

Harakannanavar, Sunil S.; Prashanth, C. R.; Patil, Sapna; Raja, K B

doi:10.1007/978-981-10-8797-4_57

Cited by 6 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM is employed as the classifier. (14) developed an algorithm that utilizes Discrete Wavelet Transform (DWT) for feature extraction in analyzing face and iris data features. The extracted features are used to form feature vectors, which are then employed to classify the patient samples.…”

Section: Fig 2 Fusion Levels In Multimodal Biometric Systemsmentioning

confidence: 99%

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

Kumari,

Nagapushpa,

Jayalaxmi

et al. 2023

IJST

View full text Add to dashboard Cite

Objectives : To develop an efficient algorithm for face and iris multimodal traits on ORL and CASIA dataset and to increase the performance rate and decrease the error rate of the model. The main goal is to increase the performance rate and decrease the error rate of the model. Methods: The proposed algorithm utilizes a fusion of face and iris modalities using Stationary Wavelet Transform (SWT) and Local Binary Pattern (LBP) techniques. The Principal Component Analysis (PCA) is applied to reduce the dimensionality of each sample, improving efficiency while preserving the most relevant information. The relevant characteristics from both face and iris modalities are fused to create a comprehensive pattern for an individual. Findings: The obtained features are compared with the features of the database images using a Euclidean Distance classifier. The performance of the proposed model is evaluated using the ORL and CASIA iris datasets. The accuracy achieved by the proposed algorithm is 99.42%, demonstrating robustness. Novelty: The algorithm introduces feature-level fusion, combining the characteristics of both face and iris modalities. The model encompasses the training and recognition phases within a biometric system. During the training phase, the biometric modality is captured and processed using the fusion of SWT+LBP+PCA techniques to form a template for each user. These templates are later stored in the database for recognition purposes.

show abstract

Section: Fig 2 Fusion Levels In Multimodal Biometric Systemsmentioning

confidence: 99%

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

Kumari,

Nagapushpa,

Jayalaxmi

et al. 2023

IJST

View full text Add to dashboard Cite

show abstract

“…As compared to traditional approaches the proposed approach gave high accuracy by comparing results on Extend Yale B, ORL, and CMU-PIE dataset. Sunil S et al [9], in this paper, a hybrid solution is presented for face recognition by using SWT, LTP, and DCT. Initially, image is resized and features are extracted using SWT and DCT.…”

Section: Related Workmentioning

confidence: 99%

Texture Analytics for Accurate Person Recognition: A Multimodal Approach

Suchetha¹,

M²

2023

IJACSA

View full text Add to dashboard Cite

Securing the resources is a most challenging task in the digital era. Traditionally, password and ID card systems were used to provide security. Password and ID cards can be stolen or hacked; to overcome this drawback biometric systems are used to authenticate the user to access the data or resources. Biometric system uses physical and behavioral characteristics of the user. Biological characteristics of the person like face, fingerprint, iris, palm print, voice, hand geometry etc. cannot be stolen and misused. Even though unimodal biometric system is more secure as compared to the traditional approach, it is not able to handle intra-class, inter-class variations, noisy data and spoofing attack. These problems can be solved using multimodal biometrics. In this paper, we discuss unimodal biometric system using Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). We propose a feature level fusion of face and fingerprint biometric traits using LTP. The implementation of the introduced system stands in comparison to the unimodal LBP and LTP for face and fingerprint system. The system is tested on ORL, UMIST, VISA face dataset and FVC fingerprint dataset. Experimental results show that the multimodal biometric system using LTP gives better accuracy as compared to the unimodal biometric system.

show abstract

“…Orthogonal transformations have very useful properties in solving science and engineering problems. Just like the Fourier and Chebyshev series which are effective methods to project a periodic function into a series of linearly independent terms, orthogonal polynomials provide a natural way to solve the related problems, such as compression and protection in image processing [1]- [3], pattern recognition [4], [5] and feature capturing [6], [7]. It also can be applied for temporal video segmentation [8], face recognition [9], and character recognition [10].…”

Section: Introductionmentioning

confidence: 99%

A General Method for Generating Discrete Orthogonal Matrices

Chan

2021

IEEE Access

View full text Add to dashboard Cite

Discrete orthogonal matrices have applications in information coding and cryptography. It is often challenging to generate discrete orthogonal matrices. A common approach widely in use is to discretize continuous orthogonal functions that have been discovered. The need of such continuous functions is restrictive. Polynomials, as the simplest class of continuous functions, are widely studied for their orthogonalality, to serve the purpose of generating orthogonal matrices. However, beginning with continuous orthogonal polynomials still takes much work. To overcome this complexity while improving the efficiency and flexibility, we present a general method for generating orthogonal matrices directly through the construction of certain even and odd polynomials from a set of distinct positive values, bypassing the need of continuous orthogonal functions. We present a constructive proof by induction that not only asserts the existence of such polynomials, but also tells how to iteratively construct them. Besides the derivation of the method as simple as a few nested loops, we discuss two well-known discrete transforms, the Discrete Cosine Transform and the Discrete Tchebichef Transform, how they can be achieved using our method with the specific values, and show how to embed them into the transform module of video coding. By the same token, we also give the examples for generating new orthogonal matrices from arbitrarily chosen values. The demonstrative experiments indicate that our method is not only simpler to implement, but also more efficient and flexible. It can generate orthogonal matrices of larger sizes, compared with those existing methods.

show abstract

Face Recognition Based on SWT, DCT and LTP

Cited by 6 publications

References 2 publications

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

Texture Analytics for Accurate Person Recognition: A Multimodal Approach

A General Method for Generating Discrete Orthogonal Matrices

Contact Info

Product

Resources

About