عرض تفاصيل البحث

ادناه جميع التفاصيل الخاصة بالبحث المطلوب عرضه. توفر هذه المنصة معلومات اساسية حول البحث, لمزيد من التفاصيل يرجى متابعة التصفح من خلال الضغط على الرابط الاصلي للبحث او رابط DOI.

عنوان البحث
Head Pose Estimation Based On Detecting Facial Features
عنوان المجلة
International Journal Of Multimedia And Ubiquitous Engineering
ISSN-1975-0080
تفاصيل النشر
سنة النشر - 2015 / الفهرس الاصلي للمجلة - 10 : 3 (عدد الصفحات 11)
تصنيف البحث
computer science - المجموعة العلمية
رابط DOI
http://dx.doi.org/10.14257/ijmue.2015.10.3.28
رابط البحث
فتح في موقع المجلة الرسمي
البحث والاستدامة
الهدف 9– الصناعة والابتكار والبنية التحتية   المزيد حول هذا الهدف
البحث والمجتمع
نعم , يدعم
الكلمات المفتاحية
Face detection, Head-pose estimation, Geometric feature, AdaBoost learning function

اسم الباحثجهة الانتساببلد الباحث
Hiyam Hatem University of Sumer Iraq
Raed Majeed University of Sumer Iraq
Zou Beiji central south university china

Head pose estimation is recently a more popular area of research. Challenging conditions, such as extreme pose, lighting, and occlusion, has historically hampered traditional, model-based methods. This paper presents a proposal of an integrated method for head pose estimation based on face detection and tracking. This method first locates certain facial features and based on their relative locations determine the head pose, the head pose estimated using coordinates of both eyes and a mouth relative to the nose as the origin of the coordinate system. The nose position is set up as the origin. The coordinates of the other parts defined from the origin, the distance between the face parts normalized so that the coordinates are independent of the image size. For facial feature detection from the detected face region, Haar-like feature utilized along with AdaBoost learning, the Adaboost learning algorithm used for creating optimized learning data. From the experiments, the proposed approach shows robustness in face and facial feature detection and eventually produces better results in estimating head pose rather than simply using Haar-like feature for both face and facial feature detection. The computational cost is low because it uses only those three points.
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