ANovel Method for Eye Blink Recognition Based on Image Processing and Artificial Neural Networks با word
ANovel Method for Eye Blink Recognition Based on Image Processing and Artificial Neural Networks با word دارای 7 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است
فایل ورد ANovel Method for Eye Blink Recognition Based on Image Processing and Artificial Neural Networks با word کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه و مراکز دولتی می باشد.
توجه : در صورت مشاهده بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی ANovel Method for Eye Blink Recognition Based on Image Processing and Artificial Neural Networks با word،به هیچ وجه بهم ریختگی وجود ندارد
بخشی از متن ANovel Method for Eye Blink Recognition Based on Image Processing and Artificial Neural Networks با word :
سال انتشار : 1395
نام کنفرانس یا همایش : اولین کنفرانس بین المللی چشم انداز های نو در مهندسی برق و کامپیوتر
تعداد صفحات : 7
چکیده مقاله:
Eye blink recognition is widely useful in many applications, such as human-computer interface,driver awareness detection, and so on. Artificial Neural networks (ANNs) are used in image processing and classifications. In this paper a new algorithm proposed which takes RGB image as input containing a face that will recognize using a conventional face recognition method. Then an image enhancement algorithm will apply to specified face region to prepare it for eye region detection. The next step is find the eyes location which implemented using proposed image processing approach. The obtained region will extract from the original image and convert to YCbCr color space to use as input for our MLP classifier. In this study an eye blinking recognition algorithm using image processing and MLP neuralnetwork has proposed including image processing method which finds the eyes regions and the ANN classifier to define whether the eyes are open or close. Moreover the proposed method can recognize winking by recognizing the blinking for each eye separately. The obtained recognition results show that this approach significantly outperforms recognition using proposed algorithm. For selected images from the CAS-PEAL face database, the averaged recognition accuracy is about 86%
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