دوره 15، شماره 2 - ( 6-1397 )                   جلد 15 شماره 2 صفحات 69-88 | برگشت به فهرست نسخه ها


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Mohammadi Dashti M, Harouni M. Smile and Laugh Expressions Detection Based on Local Minimum Key Points. JSDP. 2018; 15 (2) :69-88
URL: http://jsdp.rcisp.ac.ir/article-1-658-fa.html
محمدی دشتی مینا، هارونی مجید. آشکارسازی حالات لبخند و خنده چهره افراد بر پایه نقاط کلیدی محلی کمینه. پردازش علائم و داده‌ها. 1397; 15 (2) :69-88

URL: http://jsdp.rcisp.ac.ir/article-1-658-fa.html


دانشگاه آزاد اسلامی واحد دولت آباد، دانشکده مهندسی کامپیوتر
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در این مقاله، آشکارسازی حالات لبخند و خنده چهره با رویکرد توصیف و کاهش بُعد نقاط کلیدی ارائه شده‌است. اساس کار در این پژوهش بر مبنای دو هدف استخراج نقاط محلی کلیدی و ویژگی ظاهری آنها، و همچنین کاهش وابستگی سامانه به آموزش نهاده شده‌است. برای تحقق این اهداف سه سناریوی مختلف استخراج ویژگی‌ ارائه شده است. ابتدا اجزای یک صورت توسط الگوریتم الگوی دودویی محلی آشکار می‌شود؛ سپس در سناریوی نخست، با توجه به تغییرات همبستگی پیکسل‌های مجاور بافت محدوده لب، مجموعه نقاط کلیدی محلی بر پایه گوشه‌یاب هریس استخراج می‌شود. در سناریوی دوم، کاهش بعد نقاط مستخرج سناریوی نخست با بهبود الگوریتم تحلیل مؤلفه‌های اصلی انجام می‌شود؛ و در سناریوی آخر با مقایسه مختصات نقاط مستخرج از سناریوی نخست و توصیف‌گر بریسک مجموعه نقاط بحرانی استخراج می‌شود. در ادامه بدون آموزش سامانه، با مقایسه شکل و فاصله هندسی نقاط محلی محدوده لب حالات چهره آشکار می‌شود. برای ارزیابی روش پیشنهادی، از پایگاه داده‌های استاندارد و شناخته‌شده Cohn-Kaonde،CAFE، JAFFE و Yale  استفاده شده‌است. نتایج به‌دست‌آمده از سناریوهای مختلف به‌ترتیب بیان‌گر بهبود 33/6 و 46/16 درصدی متوسط نرخ دقت بازشناسی سناریوی دوم نسبت به نخست و سناریوی سوم نسبت به دوم است. همچنین نتایج کلی آزمایش‌ها، کارایی قابل قبول بالای 90 درصد روش پیشنهادی را نشان می‌دهد.
 

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نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش تصویر
دریافت: ۱۳۹۶/۱/۱۱ | پذیرش: ۱۳۹۷/۲/۲۶ | انتشار: ۱۳۹۷/۶/۲۶ | انتشار الکترونیک: ۱۳۹۷/۶/۲۶

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