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

M Vasanthi¹, K Seetharaman²

¹Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia.

Soft Computing

|January 18, 2021

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A new biometric face detection method, ARBBPNN, uses an autoregressive model with Bayes backpropagation neural networks. This approach effectively extracts texture and shape features for accurate face recognition, outperforming existing methods.

Area of Science:

Computer Vision
Biometrics
Machine Learning

Background:

Biometric face detection is crucial for security and identification.
Existing methods face challenges with variations in lighting, pose, and expression.
A robust and accurate face detection system is highly desirable.

Purpose of the Study:

To propose a novel autoregressive model with Bayes backpropagation neural network (ARBBPNN) for biometric face detection.
To develop a method for extracting combined texture and shape features for enhanced face recognition.
To evaluate the performance of the proposed ARBBPNN method against existing techniques.

Main Methods:

Color face images are modeled using HSV and YCbCr color spaces, forming a hybrid HS-YCbCr model.

Keywords:

Autocorrelation coefficient Face recognition Multivariate parametric test Shape feature Texture feature

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The Bayes backpropagation neural network (BBPNN) estimates model parameters within sliding windows to compute autocorrelation coefficients (ACCs).

Texture and shape features are extracted, formulated into feature vectors (FV), and combined for holistic face representation.

Main Results:

The method successfully distinguishes between texture and shape features based on ACC significance tests.
Combined feature vectors (FV) are generated for key and target face images.
Multivariate statistical tests are employed to assess the similarity between key and target FVs, determining identity.

Conclusions:

The proposed ARBBPNN method demonstrates superior performance in biometric face detection compared to existing approaches.
The integration of texture and shape features through the hybrid model enhances recognition accuracy.
Experimental validation on multiple datasets confirms the efficacy of the ARBBPNN technique.