Fusion Based Face Recognition System using 1D Transform Domains

Halvi, Srinivas; Ramapur, Nayina; Raja, K B; Prasad, Shanti

doi:10.1016/j.procs.2017.09.095

Cited by 9 publications

(8 citation statements)

References 7 publications

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“…These parameter values of DTCWT, QFT and fusion of these mechanisms are recorded in Table 1 , with various combinations of HID and HOD values. The Table 2 gives the results recorded towards performance comparison of proposed fusion techniques with the existing mechanisms presented by researchers in (29) , (30) , (31) and (32) . The percentage values of optimum TSR are higher and percentage https://www.indjst.org/ of Equal Error Rate values are less in proposed technique when compared with available techniques.…”

Section: Resultsmentioning

confidence: 99%

“…The graphical illustration of Total Success Rate and Equal Error Rates are demonstrated in Figures 8 and 9. The rates of recognition and Half error of proposed technique is compared with the and BSIF+TLPP(7x7), FFT+DWT techniques presented in the work (30) and (29) . The graphical analysis given in Figures 10 and shows that the proposed technique's performance is better placed.…”

Section: Resultsmentioning

confidence: 99%

“…As mentioned in the explanation of results above, the obtained results are compared with various related researches on speech recognition strategies such as RASTA+LPC+DWT (34) , LPCC+MFCC (35) , SVM (36) , HMM (37) and RNN (38) . The performance evaluation of proposed fusion technique for face recognition is done with various existing techniques proposed by different researchers such as FFT+DWT (27) , LBP +DWT +SOM (28) , DWT +SVM (29) and RLM for Canny Edge (30) . The results demonstrated that the proposed fusion techniques for both speech and face recognitions placed better in the performance.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

Shanthakumar¹,

Nagaraja²,

Basthikodi³

2021

IJST

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Objectives:The main objective is to propose a multimodal biometric system by forming a fusion of Face and Speech modalities using DTCWT+QFT techniques for face and MFCC+RASTA Techniques for Speech recognitions. The experimental results are compared with existing works and analysed the performance with counterparts. Methods: The proposed model, make use of DTCWT and QFT techniques to extract the features of face images and perform fusion of both. The MFCC and RASTA techniques are implemented to extract features of speech data and then fusion is applied. Various databases discussed and utilized for both face and speech recognition system proposed. Findings: The results of experimentation are compared with existing systems and analysis proved than the proposed system is placed in better position. The fusion of DTCWT and QFT techniques for face recognition system is implemented and the results using performance parameters such as False Acceptation Ratio (FAR), False Rejection Ratio (FRR), Total Success Rate (TSR), Partial Error Rate (PER), Equal Error Rate (EER) are tabulated for six different types of face data sets. The average performance of the results is compared with four existing fusion techniques and showed that the proposed system performs better. The fusion of MFCC and RASTA techniques for speech recognition system is implemented and the performance is measured by calculating accuracy, precision, recall and F1-score. These results are compared with five different schemes and proved that proposed system of fusion of face and speech traits works better for human recognitions. Novelty: Fusion of two algorithms for face recognition is implemented and the results analysed. Then the fusion of two algorithms for speech recognition is implemented and the results are analysed. The novel approach is presented to combine both face and speech recognition system in to single system to improve the security using multimodal biometrics.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

Shanthakumar¹,

Nagaraja²,

Basthikodi³

2021

IJST

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“…This is because face recognition is an easy way to distinguish people in a short time and with good accuracy. Halvi et al [8], the authors suggested a way to distinguish faces through one-dimensional (1D) transform domain, which is optioned by transforming the two-dimensional image face into 1D. The features extraction from discrete wavelet transform (DWT) and fast Fourier transform (FFT) are compared with dataset using Euclidian distance (ED) to distinguish faces.…”

Section: Introductionmentioning

confidence: 99%

Half-face based recognition using principal component analysis

Alkababji

Raed²

2021

IJEECS

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<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

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“…Reference [4] introduced another face recognition system using 1D transform domains. The authors benefitted from the combination of discrete wavelet transform and fast Fourier transform for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

Alazzawi

Uçan

Bayat

2018

Mathematical Problems in Engineering

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Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

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Fusion Based Face Recognition System using 1D Transform Domains

Cited by 9 publications

References 7 publications

System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

Half-face based recognition using principal component analysis

Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

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