انتخاب زیرمجموعه بهینه از ویژگی‌های استخراج‌شده توسط عملگر بهینه‌شده LBP بر مبنای CLA - EC در سیستم بازشناسی چهره
محورهای موضوعی : مهندسی برق و کامپیوتر
اختر حضرتی بی‌شک
1
(دانشگاه آزاد اسلامی واحد اهر)
کریم فائز
2
(دانشگاه صنعتی امیرکبیر)
حسین برقی جند
3
(دانشگاه آزاد اسلامی واحد اهر)
سجاد قطعی
4
(دانشگاه پیام نور تبریز)
کلید واژه: آتاماتای یادگیر سلولی الگوی باینری محلی ماشین بردار پشتیبان محاسبات تکاملی,
چکیده مقاله :
ما در اين مقاله روش کارامد جديدی را مبتنی بر توصيفگر الگوی باينری محلی برای بازشناسی چهره معرفی کرديم. چون محاسبات داخل الگوی باینری محلی بین مقادیر دو پیکسل انجام می‌شود، حتی تغییرات کوچک در الگوی باینری عملکرد آن را تحت تأثیر قرار می‌دهد. در این مقاله یک روش جدید بازشناسی چهره برای انتخاب الگوهای باینری میانگین محلی (LABP) بر مبنای آتاماتای یادگیر سلولی مبتنی بر محاسبات تکاملی ارائه شده است. در روش پیشنهادی، ابتدا الگوهای باینری یکنواخت محلی توسط LABP از تصاویر چهره استخراج می‌شود. در LABPجهت به دست آوردن نمایش ویژگی مقاوم‌تر، نقاط نمونه زیادی مورد استفاده قرار گرفته است، سپس بهترین زیرمجموعه از این الگوها بدون داشتن اطلاعات اولیه از آنها توسط روش CLA-ECپیدا شده و از آنها هیستوگرام گرفته می‌شود و در نهایت از ماشین بردار پشتیبان برای طبقه‌بندی استفاده می‌شود. نتیجه به دست آمده از شبیه‌سازی سیستم‌های بازشناسی چهره روی مجموعه داده FERET، برتری الگوریتم پیشنهادی را نسبت به الگوریتم‌های دیگر نشان داد.
In this paper, we present a new efficient method based on local binary pattern descriptor, for face recognition. Because, the calculations in Local binary pattern are done between two pixels values, so, small changes in the binary pattern affect its performance. In this paper, a new local average binary pattern descriptor is presented based on cellular learning automata and evolutionary computation (CLA-EC). In the proposed method, first, the LABP operator are used to extract uniform local binary patterns from face images; it should be noted that, in LABP operator to obtain more robust feature representation, many sample points has been used. Then, the best subset of patterns found by CLA-EC methods, and the histogram of these patterns is obtained. Finally, support vector machine is used for classification. The results of experiment on FERET data base show the advantage of the proposed algorithm compared to other algorithms.
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