تشخیص چهره در تصویر با استفاده از روش ویولا-جونز و تحلیل بافت تصویر
محورهای موضوعی : سامانه های پردازشی و ارتباطی چند رسانه ای هوشمند
مهدی حریری
1
(استادیار، گروه مهندسی برق و کامپیوتر ، دانشگاه زنجان، زنجان، ایران)
نرمینه حیدرزاده
2
(کارشناسی ارشد، مهندسی کامپیوتر، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران)
کلید واژه: تصویر یکپارچه, تشخیص چهره, الگوریتم ویولا-جونز, تطبیق چشم و بینی, شبکه عصبی رگرسیونی,
چکیده مقاله :
شناسایی چهره از مهمترین فناوری‌های بیومتریک برای شناسایی افراد است که در کنترل دسترسی هم استفاده دارد. تشخیص چهره یکی از مراحل مهم قبل از شناسایی هویت می باشد. معمولا برای تشخیص وجود چهره در تصاویر از یک روش استفاده شده است، اما در این تحقیق برای افزایش صحت تشخیص از ترکیب دو روش الگوریتم ویولا-جونز و تطبیق اجزا و بافت تصویر با اجزا چهره و پوست برای بهبود عملکرد تشخیص استفاده میشود. در مرحله اول با استفاده از الگوریتم ویولا-جونز به تشخیص اجزای صورت می‌پردازیم و در مرحله بعدی با شبکه های عصبی رگرسیونی ویژگی های بافت چشم و بینی را مورد بررسی قرارداده و باتطبیق ویژگی های بافت صورت اجزاء صورت بهتر تشخیص داده می شوند. در این تحقیق از ویژگی های بافت مربوط به چشم راست و چپ و بینی درصورت برای افزایش دقت تطبیق استفاده می شود. برای ارزیابی روش پیشنهادی از چهره های مجموعه داده FDD-Fold استفاده نموده‌ایم. در مقایسه عملکرد این روش با روش شبکه عمیقRCNN با تعداد خیلی کمتر داده های آموزشی نسبت به آن به صحت 96.36% ، بیشتر از شبکه‌ی یادگیری عمیق رسیدیم. این روش در سیستمهای با توانایی محاسباتی محدود با تعداد داده‌ی متوسط نتیجه مطلوبی می دهد.
Introduction: Face recognition is one of the most important biometric technologies for people identification, also used in access control. Face recognition is one of the important steps before identity recognition. Usually, one method is used to detect the presence of faces in images. Still, in this research, to increase the accuracy of detection, the combination of two methods is used to improve the detection performance: Viola-Jones algorithm and the matching of image components and texture with face and skin components. In the first step, we use the Viola-Jones algorithm to detect the facial features. In the next step, the features of the eye and nose tissues are analyzed with regression neural networks, and facial features are recognized better according to the facial features. In this research, the texture features of the right and left eyes and the nose of the face are used to increase the matching accuracy. We have used the faces of the FDD-Fold dataset to evaluate the proposed method. Comparing the performance of this method with the RCNN deep network method with a much smaller number of training data, we reached an accuracy of 96.36%, more than the deep learning network. This method gives good results in systems with limited computing ability and average amount of data.The face recognition system is one of the biometric identification systems and one of the most important technologies for people identification, which is also used in access control. Face identification is one of the few biometric methods that, with the advantages of high accuracy and low level of human intervention, is used in cases such as information security, law enforcement and monitoring, traffic control, and registration in attendance systems. This method creates more convenience and development with fewer requirements. then, this method has received more attention during the last twenty years.Face detection is a local binary classification problem that shows the presence of faces in the given image using boxes surrounding them. Although the Viola-Jones method is less accurate than modern methods such as convolutional neural networks; Its much lower efficiency and training parameters compared to the millions of parameters of a typical CNN result in faster training, better accuracy with limited data, and its use in devices with limited computing power such as cameras and mobile phones. The innovation of this method is matching the geometric pattern of the edges to identify the presence of the face in the image, along with matching the skin texture. This method seems to be faster and more accurate than the previous ones.Method: In this research, in the first step, we use Viola-Jones, one of the optimal face recognition algorithms in the image, to detect facial components. In the next step, we use the adaptation of the general shape of facial parts such as eyes, and match the textures in the image with the predicted texture for human skin, to improve the recognition performance and increase the recognition accuracy, in such a way that the regression neural networks examine the eye and nose tissue characteristics and according to the characteristics of the facial tissue, the facial components are recognized by the regression neural network. The investigated features in the texture include minimum and maximum color intensity, mean and median, and variance of the image. The data is given to the regression neural network for training. Here Remarkable thing is matching the overall shape of the human head and face, and in the next step matching the overall shape of the facial parts such as the eyes to improve the accuracy of the presented method. We also use the matching of textures in the image with the texture predicted for human skin to further improve the accuracy of the program's performance.
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