A Hybrid Method of Face Detection Based on Feature Optimization and Neural Network

Abstract

The advancement of digital technology needs biometric security systems. Face detection plays an essential role in the security of digital devices. The detection of a face based on the lower content of the facial image for the processing of detection. In this paper modified the BP Neural Network Model for the detection of the human face. The modification of face detection algorithms incorporates feature optimization. The feature optimization process reduces the distorted features of the facial image. The optimized features of facial image enhance the performance of face detection for the optimization of features used glowworm optimization algorithms. The glowworm optimization algorithm is a dynamic population-based search technique. The concept of glowworm is a neighbor’s selection of worms based on the process of lubrification. For feature extraction we use discrete wavelet transform. The discrete wavelet transform function drives the features component in terms of low frequency and high frequency of facial image. The proposed algorithm simulated in MATLAB software and used a reputed facial image dataset from CSV300. Our experimental results show a better detection rate instead of the BP neural network model.