آشکارسازی صورت با استفاده از فیلترهای گابور و شبکه‌های عصبی
محورهای موضوعی : مهندسی برق و کامپیوترمحمود محلوجی 1 , رضا محمدیان 2 *
1 - دانشگاه آزاد اسلامی، واحد كاشان
2 - دانشگاه آزاد اسلامی، واحد كاشان
کلید واژه: آشکارسازی صورت شبکه عصبی فیلتر گابور ویژگی‌های گابور,
چکیده مقاله :
در این مقاله، روشی قدرتمند برای آشکارسازی صورت از زوایای مختلف با استفاده از ترکیب فیلترهای گابور و شبکه عصبی بیان می‌شود. در ابتدا رابطه ریاضی تولید فیلتر گابور مورد بررسی قرار می‌گیرد و در مرحله بعد با بررسی 75 بانک فیلتر مختلف، محدوده مقادیر پارامترهای مؤثر در تولید فیلتر گابور مشخص شده و سپس بهترین مقدار برای آنها به دست می‌آید. شبکه عصبی مورد استفاده در این مقاله از نوع پیش‌خور با روش بازگشتی است و بردار ورودی این شبکه عصبی از کانوالو تصویر با تنها یک فیلتر گابور با زاویه 2/ و فرکانس 2/ در حوزه فرکانس به دست می‌آید. الگوریتم پشنهادی در این مقاله روی 550 تصویر از 2 پایگاه تصویر فرت با پس‌زمینه ساده و مارکوس وبر با پس‌زمینه پیچیده آزمایش شده و دقت آشکارسازی آن به ترتیب 4/98% و 95% است. همچنین به کمک الگوریتم ویولا جونز ناحیه صورت را در 550 نمونه تصویر به دست آورده و مقایسه‌ای بین نتایج به دست آمده از الگوریتم ویولاجونز و الگوریتم پیشنهادی آورده می‌شود.
In this paper, a robust method for face detection from different views using a combination of Gabor filters and neural networks is presented. First, a mathematical equation of Gabor filter is expressed. Then, by examining 75 different filter banks, range of effective parameters values in Gabor filter generation is determined, and finally, the best value for them is specified. The neural network used in this paper is a feed-forward back-propagation multilayer perceptron network. The input vector of the neural network is obtained from the convolution the input image and a Gabor filter with angles π / 2 and the frequency π / 2 in the frequency domain. The proposed method has been tested on 550 image samples from Feret database with simple background and Markus Weber database with complex background, and detection accuracy of them is 98.4% and95%, respectively. Also, the face area has been detected using Viola-Jones algorithm, and then comparison between the results obtained from Viola-Jones algorithm and the proposed method is described.
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