A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

Abstract

This paper proposes a novel method, which is coined as ARBBPNN, for biometric-oriented face detection, based on autoregressive model with Bayes backpropagation neural network (BBPNN). Firstly, the given input colour key face image is modelled to HSV and YCbCr models. A hybrid model, called HS-YCbCr, is formulated based on the HSV and YCbCr models. The submodel, H, is divided into various sliding windows of size, 3 9 3. The model parameters are estimated for the window using the BBPNN. Based on the model coefficients, autocorrelation coefficients (ACCs) are computed. An autocorrelation test tests the significance of the ACCs. If the ACC passes the test, then it is inferred that the small image region, viz. the window, represents the texture and it is treated as the texture feature. Otherwise, it is regarded as structure, which is treated as the shape feature. The texture and shape features are formulated as feature vectors (FV) separately, and they are combined into a single FV. This process is performed for all colour submodels. The FVs of the submodels are combined into a single holistic vector, which is treated as the FV of the key face image. The key FV has twenty feature elements. The similarity of the key and target face images is examined, based on the key and target FVs, by deploying multivariate parametric statistical tests. If the FVs of the key and target images pass the tests, then it is concluded that the key and target face images are the same; otherwise, they are regarded as different. The GT, FSW, Pointing'04, and BioID datasets are considered for the experiments. In addition to the above datasets, we have constructed a dataset with face images collected from Google, and many images captured through a digital camera. It is also subjected to the experiment. The obtained recognition results show that the proposed ARBBPNN method outperforms the existing methods.

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Facial and Palm-Based Biometric Authentication and Data Security

“…The remaining part is arranged as: several works associated with the proposed methodology is elucidated in section 2; the proposed system is presented in section 3; the performance of the proposed methodologies is delineated in section 4; the paper is wrapped up in section 5. (Vasanthi & Seetharaman, 2021) built a technique for biometric-driven facial image recognition centered on multivariate correlation analysis. The features were engendered as a feature tensor matrix.…”

Satellite Image Retrieval Based on Adaptive Gaussian Markov Random Field Model with Bayes Back-Propagation Neural Network

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“…The ANN surpasses the conventional Bayesian approach and other iterative techniques in pattern classification and segmentation because of the flexibility and suitability of the ANN technique for nonlinearity and dimensionality reduction [14][15][16][17]. The ANN, either independently or combined with the Bayesian rule, provides better results for image analysis [15][16][17][18][19][20]. Si and He [17] have applied an ANN technique to estimate the parameters of the autoregressive model and have reported that the ANN-based parameter estimate yields better results than the maximum likelihood, Bayes, and iterative methods.…”