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Mohammadian, R., Mahlouji, M., Shahidinejad, A.. (1399). Multi-View Face Detection in Open Environments using Gabor Features and Neural Networks. , 8(4), 461-470. doi: 10.22044/jadm.2020.8853.2019
R. Mohammadian; M. Mahlouji; A. Shahidinejad. "Multi-View Face Detection in Open Environments using Gabor Features and Neural Networks". , 8, 4, 1399, 461-470. doi: 10.22044/jadm.2020.8853.2019
Mohammadian, R., Mahlouji, M., Shahidinejad, A.. (1399). 'Multi-View Face Detection in Open Environments using Gabor Features and Neural Networks', , 8(4), pp. 461-470. doi: 10.22044/jadm.2020.8853.2019
Mohammadian, R., Mahlouji, M., Shahidinejad, A.. Multi-View Face Detection in Open Environments using Gabor Features and Neural Networks. , 1399; 8(4): 461-470. doi: 10.22044/jadm.2020.8853.2019

Multi-View Face Detection in Open Environments using Gabor Features and Neural Networks

Journal of AI and Data Mining
دوره 8، شماره 4، بهمن 2020، صفحه 461-470    اصل مقاله (1.54 M)
نوع مقاله: Original/Review Paper
شناسه دیجیتال (DOI): 10.22044/jadm.2020.8853.2019
نویسندگان
R. Mohammadian1؛ M. Mahlouji* 2؛ A. Shahidinejad1
1Department of Computer, Faculty of Engineering, Islamic Azad University, Qom Branch, Qom 3749113191, Iran.
2Department of Telecommunications, Kashan Branch, Islamic Azad University, Kashan, Iran.
چکیده
Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard deviation, aspect ratio and phase offset) for an image is analysed, secondly, the range of Gabor filter parameter values is determined and finally, the best values for these parameters are specified. A multilayer feedforward neural network with a back-propagation algorithm is used as a classifier. The input vector is obtained by convolving the input image and a Gabor filter, with both the angle and frequency values equal to π/2. The proposed algorithm is tested on 1,484 image samples with simple and complex backgrounds. The experimental results show that the proposed detector achieves great detection accuracy, by comparing it with several popular face-detection algorithms, such as OpenCV’s Viola-Jones detector.
کلیدواژه‌ها
Facial features؛ standard division؛ Gabor energy؛ face components
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